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Quantifying the effect of orbit altitude on mission cost for Earth observation satellites

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Quantifying the effect of orbit altitude on mission cost for Earth observation satellites

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Quantifying the Effect of Orbit Altitude on
Mission Cost for Earth Observation Satellites

By Anthony Shao

Dissertation Committee
Dr. Azad M. Madni (Chair)
Dr. Daniel A. Erwin (Co-Chair)
Dr. James R. Wertz
Dr. Joseph A. Kunc
Dr. Barry W. Boehm
Dr. Gregory W. Autry

A Dissertation Submitted in Partial Fulfillment
Of the Degree Requirements for the Degree of
Doctor of Philosophy
Department of Astronautical Engineering
Viterbi School of Engineering
Faculty of the USC Graduate School
University of Southern California

May 2016
2

Executive Summary
The ever-increasing cost of space missions inevitably leads to longer schedules and fewer
missions. This leads to a demand for higher reliability, which, in turn, leads to higher cost, longer
schedules, and fewer missions. This process is known as the space spiral. Space missions today
are costing too much, and within today’s budget environment, it has become a national problem.
In recent times, small commercial startups companies such as Skybox Imaging and Planet Labs
have broken this trend and have been designing and flying smaller low-cost spacecraft at lower
altitudes to provide world-wide imaging capabilities; however, with slightly lower performance
(resolution). Given a fixed set of performance requirements (resolution, coverage, and lifetime)
for an Earth observation mission, what orbit altitude will provide the lowest cost option? What is
the relationship between mission cost and orbit altitude? My research explores various sets of
performance requirements for Earth observation missions and answers these questions by
quantifying the effect orbit altitude has on total mission cost, in low Earth orbit (< 1,000 km).
This is an interdisciplinary effort that combines systems engineering, physics, and cost modeling.
Preliminary results using a baseline mission indicated that flying multiple small satellites at
lower altitudes (200 – 500 km), compared to traditional large systems has the potential for much
lower mission costs for Earth observation missions with fixed performance requirements.
Additional research further supported these findings. However, there was one exception;
performance requirements that combine both minimal resolution and high coverage rate were
optimal with small satellites flown at higher altitude regimes (700 – 1000 km). These results
have a major impact in the aerospace community and can begin changing the way business is
done in space and effectively start reversing the space spiral.
3

Acknowledgements
First and foremost, I would like to thank my dissertation committee. I want to thank Professor
Azad Madni, my Dissertation Committee Chair, who was the key reviewer of my research. I
thank him for providing me formal guidelines on how to prepare for the Qualifying Exam and
how to complete a PhD Dissertation. I want to thank him for his advisement and guidance on this
research. I cannot thank him enough for paying close attention to and caring about my research
and results. I want to thank Dr. Daniel Erwin for serving as co-chair of my Dissertation
Committee as well. I thank him for providing guidance on my research and explaining what it
means to pursue a PhD in engineering. I am grateful for his advisement, while he was serving as
the chair of the Astronautical Engineering Department. I also want to thank Dr. Gregory Autry,
Dr. Barry Boehm, and Dr. Joseph Kunc for reviewing my work and providing much useful input
and direction. I want to thank the ASTE Department administrators and advisors led by Dell
Cuason for guiding me through this process as well.
I would like to acknowledge the California Space Grant Consortium for providing seed funding
for my research, and for kick starting my research. I would also like to acknowledge the
assistance of International Cost Estimating and Analysis Association (ICEAA) which was kind
enough to let me present my preliminary results at their March 19, 2014 Workshop. Participants
at the meeting asked a number of penetrating and useful questions that substantially improved
the results. I want to thank all my colleagues at Microcosm for their support, Dr. Aaron Cote, Dr.
Michael Shindler, and the Computer Science Department for their support, and Elizabeth Koltz
for being a wonderful collaborator.
4

I want to thank all my family and friends for allowing me to remain focused throughout my
research. I apologize for all the time this research has kept me busy and out of sight for weeks
and months at a time. My thanks to my parents, brothers, sister, grandparents, and to all my
extended family for their support: The Shao’s, Alvarado’s, Yam’s, and Chen’s. Thanks to my
friends for all their constant support and encouragement: Pete Poznanski, Angelou Masters, Dean
Bergman, Jocelyn Hui, Vanessa Kuroda, Sebastien Hohl, Brian Wieber, Tommy Cross, and
everyone else supporting in spirit.
Of course, none of this could have been made possible without the following three individuals.
To my technical advisor, mentor and friend, James R. Wertz, for allowing me to pursue a
doctoral degree under his mentorship. I thank him for introduced me to this area of research,
being a great mentor in my life, and giving me the opportunity to work very challenging
problems that honed my research skills. To my mother, Jenny, for being the strongest women I
know, and for always encouraging me to pursue only what I enjoy in life. Most importantly, I
want to thank Dominick A. Berkery, for continuously supporting me, and motivating me to excel
in everything that I do. He was there every step of the way, encouraging me to work swiftly to all
my deadlines, and insisting on celebrating all my milestones. I could not have done this without
him.

5

Table of Contents
EXECUTIVE SUMMARY .......................................................................................................... 2

ACKNOWLEDGEMENTS ......................................................................................................... 3

TABLE OF CONTENTS ............................................................................................................. 5

LIST OF TABLES ........................................................................................................................ 8

LIST OF FIGURES ...................................................................................................................... 9

GLOSSARY ................................................................................................................................ 11

1
INTRODUCTION ................................................................................................................ 17

2
STATEMENT OF THE PROBLEM .................................................................................. 20

2.1.
THE PROBLEM ............................................................................................................. 20

2.2.
RESEARCH OBJECTIVES ............................................................................................... 22

3
LITERATURE REVIEW .................................................................................................... 23

3.1.
CURRENT APPROACHES TO COST REDUCTION ............................................................. 23

3.2.
COST MODELS ............................................................................................................. 25

3.2.1.
Unmanned Space Vehicle Cost Model (USCM) ........................................... 27

3.2.2.
NASA Instrument Cost Model (NICM) ........................................................ 29

3.2.3.
Small Satellite Cost Model (SSCM) ............................................................. 31

3.2.4.
Cost Model Limitations ................................................................................. 33

3.3.
ALTITUDE OF SPACECRAFT .......................................................................................... 33

3.3.1.
Advantages of Higher Altitudes .................................................................... 34

3.3.2.
Advantages of Lower Altitudes ..................................................................... 35

6

3.3.3.
Mission Cost vs. Orbit Altitude ..................................................................... 39

3.4.
SMALL VS. LARGE SATELLITES .................................................................................... 40

3.4.1.
Comparison of Advantages ........................................................................... 41

3.4.2.
Schedules, Reliability, and Risk .................................................................... 45

3.4.3.
Mission Cost .................................................................................................. 51

4
BACKGROUND OF PROPOSED RESEARCH .............................................................. 53

4.1.
PERFORMANCE REQUIREMENTS .................................................................................. 55

4.2.
MASS ESTIMATIONS .................................................................................................... 56

4.2.1.
Instrument Sizing .......................................................................................... 56

4.2.2.
Payload Mass, Power, and Data-rate ............................................................. 57

4.2.3.
Spacecraft Bus ............................................................................................... 58

4.2.4.
Redundancy ................................................................................................... 59

4.2.5.
Launch Mass .................................................................................................. 60

4.3.
ESTIMATING NUMBER OF SPACECRAFT ....................................................................... 61

4.3.1.
Coverage Rate ............................................................................................... 62

4.3.2.
Mission Lifetime ........................................................................................... 62

4.3.3.
System Redundancy ...................................................................................... 63

4.4.
COST ESTIMATION ....................................................................................................... 64

4.4.1.
Upfront Cost .................................................................................................. 64

4.4.2.
Cost Models ................................................................................................... 65

4.4.3.
Cost Estimating Relationships (CERs) .......................................................... 66

4.4.4.
Launch Cost ................................................................................................... 70

4.4.5.
Operations Cost ............................................................................................. 71

7

4.4.6.
Production Cost with Learning Curve ........................................................... 73

4.4.7.
Amortization Cost ......................................................................................... 74

4.4.8.
Total Mission Cost ........................................................................................ 75

4.4.9.
Constant-Year-Dollars ................................................................................... 75

4.5.
SUMMARY OF ASSUMPTIONS ....................................................................................... 76

5
RESEARCH HYPOTHESIS & PRELIMINARY RESULTS ......................................... 79

5.1.
RESEARCH HYPOTHESIS .............................................................................................. 79

5.2.
PRELIMINARY RESULTS ............................................................................................... 81

6
RESEARCH RESULTS AND CONCLUSIONS ............................................................... 87

6.1.
RESULTS ...................................................................................................................... 88

6.2.
SAMPLE POINT DESIGN ................................................................................................ 92

6.3.
CONCLUSIONS ............................................................................................................. 95

6.4.
IMPLICATIONS OF RESEARCH ..................................................................................... 100

6.5.
CONTRIBUTION TO SOCIETY ...................................................................................... 102

6.6.
RECOMMENDATIONS FOR FUTURE WORK .................................................................. 103

7
APPENDIX ......................................................................................................................... 105

7.1.
SELECTED DATA FOR PAST SPACECRAFT .................................................................. 105

7.2.
VARIATION IN INPUT ASSUMPTIONS .......................................................................... 108

7.3.
MISSION COST VS. ORBIT ALTITUDE FOR ALL 27 SCENARIOS ................................... 109

REFERENCES .......................................................................................................................... 123

8

List of Tables
Table 3-1. Unmanned Space Vehicle Cost Model Non-Recurring Engineering CERs ............................................... 28

Table 3-2. Unmanned Space Vehicle Cost Model Recurring Engineering CERs ....................................................... 29

Table 3-3. NASA Instrument Cost Model CERs ......................................................................................................... 30

Table 3-4. Estimated Wrap Factors CERs for NASA Instrument Cost Model ............................................................ 31

Table 3-5. Small Spacecraft Cost Model CERs ........................................................................................................... 32

Table 3-6. Difference Between Small Satellites and Large Satellites .......................................................................... 41

Table 3-7. Advantages of Small Satellites ................................................................................................................... 42

Table 3-8. Development Time Comparison for Small vs. Large Satellites ................................................................. 47

Table 3-9. Time in years from ATP to Launch for Small and Large Satellites ........................................................... 47

Table 3-10. Reliability of SmallSats Compared to All Spacecraft .............................................................................. 48

Table 3-11. SmallSat Failures by Configuration .......................................................................................................... 50

Table 3-12. Failures by Cause ...................................................................................................................................... 50

Table 4-1. Data from Existing Observation Systems ................................................................................................... 58

Table 4-2. Average Mass by Subsystem as a Percentage of Dry Mass for 4 Types of Spacecraft ............................. 59

Table 4-3. Cost Data for Existing Launch Vehicles ..................................................................................................... 71

Table 4-4. Inflation Factors Relative to the Year 2012 ................................................................................................ 76

Table 4-5. Input Data for the Baseline Mission. .......................................................................................................... 77

Table 5-1. Baseline mission requirements ................................................................................................................... 81

Table 5-2. Physical Parameters of 3 Select Mission Altitudes .................................................................................... 83

Table 5-3. Cost Predictions for the 3 Selected Altitudes using USCM8 and NICM ................................................... 83

Table 5-4. Cost Predictions for the 3 Selected Altitudes using SSCM ........................................................................ 83

Table 6-1. List of 27 Different Performance Requirement Scenarios .......................................................................... 89

Table 6-2. Requirements and Sample Missions for Selected Scenarios ...................................................................... 89

Table 6-3. Mass Estimates for Theoretical Spacecraft Designed for Scenario 14 at 400 and 800 km ........................ 93

Table 6-4. Optimal Altitude Determined for Each Scenario ........................................................................................ 99

Table 7-1. Authorization to Proceed (ATP) Date, Launch Date, and Size of Spacecraft. ......................................... 105

9

List of Figures
Figure 1-1. The Space Spiral ........................................................................................................................................ 17

Figure 1-2. Reversing the Space Spiral ........................................................................................................................ 19

Figure 3-1. Characteristics of a Cost Model ................................................................................................................ 26

Figure 3-2. Effective Surveillance Footprint Sizes ...................................................................................................... 36

Figure 3-3. Spacecraft Mass Available to Launch vs. Orbit Altitude for a Fixed Launch System .............................. 37

Figure 3-4. Spatial Density of Orbital Debris Particles in LEO ................................................................................... 38

Figure 3-5. Monthly Effective Mass of Objects in Earth Orbit by Region .................................................................. 38

Figure 3-6. Theoretical Reliability of Single vs. Redundant Configurations ............................................................... 49

Figure 5-1. Total Mission Cost vs. Orbit Altitude for Fixed Performance Requirements ........................................... 85

Figure 6-1. Mission Cost vs. Orbit Altitude for Scenario 1 ......................................................................................... 89

Figure 6-2. Mission Cost vs. Orbit Altitude for Scenario 8 ......................................................................................... 90

Figure 6-3. Mission Cost vs. Orbit Altitude for Scenario 14 ....................................................................................... 90

Figure 6-4. Mission Cost vs. Orbit Altitude for Scenario 18 ....................................................................................... 91

Figure 6-5. Mission Cost vs. Orbit Altitude for Scenario 24 ....................................................................................... 91

Figure 6-6. Mission Cost vs. Orbit Altitude for Scenario 27 ....................................................................................... 92

Figure 6-7. Flock of Planet Labs CubeSats .................................................................................................................. 94

Figure 6-8. MPS-130™ CubeSat High-Impulse Adaptable ......................................................................................... 94

Figure 6-9. Reversing the Space Spiral ...................................................................................................................... 100

Figure 7-1. Mission Cost vs. Orbit Altitude Over a Range of Learning Curves ........................................................ 108

Figure 7-2. Mission Cost vs. Orbit Altitude for Scenario 1 ....................................................................................... 109

Figure 7-3. Mission Cost vs. Orbit Altitude for Scenario 2 ....................................................................................... 110

Figure 7-4. Mission Cost vs. Orbit Altitude for Scenario 3 ....................................................................................... 110

Figure 7-5. Mission Cost vs. Orbit Altitude for Scenario 4 ....................................................................................... 111

Figure 7-6. Mission Cost vs. Orbit Altitude for Scenario 5 ....................................................................................... 111

Figure 7-7. Mission Cost vs. Orbit Altitude for Scenario 6 ....................................................................................... 112

Figure 7-8. Mission Cost vs. Orbit Altitude for Scenario 7 ....................................................................................... 112

10

Figure 7-9. Mission Cost vs. Orbit Altitude for Scenario 8 ....................................................................................... 113

Figure 7-10. Mission Cost vs. Orbit Altitude for Scenario 9 ..................................................................................... 113

Figure 7-11. Mission Cost vs. Orbit Altitude for Scenario 10 ................................................................................... 114

Figure 7-12. Mission Cost vs. Orbit Altitude for Scenario 11 ................................................................................... 114

Figure 7-13. Mission Cost vs. Orbit Altitude for Scenario 12 ................................................................................... 115

Figure 7-14. Mission Cost vs. Orbit Altitude for Scenario 13 ................................................................................... 115

Figure 7-15. Mission Cost vs. Orbit Altitude for Scenario 14 ................................................................................... 116

Figure 7-16. Mission Cost vs. Orbit Altitude for Scenario 15 ................................................................................... 116

Figure 7-17. Mission Cost vs. Orbit Altitude for Scenario 16 ................................................................................... 117

Figure 7-18. Mission Cost vs. Orbit Altitude for Scenario 17 ................................................................................... 117

Figure 7-19. Mission Cost vs. Orbit Altitude for Scenario 18 ................................................................................... 118

Figure 7-20. Mission Cost vs. Orbit Altitude for Scenario 19 ................................................................................... 118

Figure 7-21. Mission Cost vs. Orbit Altitude for Scenario 20 ................................................................................... 119

Figure 7-22. Mission Cost vs. Orbit Altitude for Scenario 21 ................................................................................... 119

Figure 7-23. Mission Cost vs. Orbit Altitude for Scenario 22 ................................................................................... 120

Figure 7-24. Mission Cost vs. Orbit Altitude for Scenario 23 ................................................................................... 120

Figure 7-25. Mission Cost vs. Orbit Altitude for Scenario 24 ................................................................................... 121

Figure 7-26. Mission Cost vs. Orbit Altitude for Scenario 25 ................................................................................... 121

Figure 7-27. Mission Cost vs. Orbit Altitude for Scenario 26 ................................................................................... 122

Figure 7-28. Mission Cost vs. Orbit Altitude for Scenario 27 ................................................................................... 122

11

Glossary
Acronyms
AAR – Area Access Rate
APL – John Hopkins Applied Physics Laboratory
ATP – Authorization to Proceed
CAD – Cost Analysis Division (NASA HQ)
CER – Cost Estimating Relationship
COT – Commercial off-the Shelf
DARPA - Defense Advanced Research Projects Agency
DFU – Development Plus First Unit Cost
DoD – Department of Defense
FoM – Figure of Merit
FoR – Field of Regard
FY – Fiscal Year
GEO – Geosynchronous Orbit
GPS – Global Positioning System
GSE – Ground Support Equipment
GSFC – Goddard Space Flight Center (NASA)
GTO – Geosynchronous Transfer Orbit
HEO – High Earth Orbit
IA&T – Integration, Assembly, and Testing
ICEAA – International Cost Estimating and Analysis Association
IPAO – Independent Program Assessment Office
12

JPL – Jet Propulsion Laboratory (NASA)
JWST – James Webb Space Telescope
LEO – Low Earth Orbit
MILSTD – Military Standard
MEO – Medium Earth Orbit
MoE – Measure of Effectiveness
MSL – Mars Science Laboratory
NAFCOM – NASA/Air Force Cost Model
NASA – National Aeronautics and Space Administration
NICM – NASA Instrument Cost Model
NRE – Non-recurring Engineering
OSD – Office of Secretary of Defense
PA – Product Assurance
PBCM – Performance-Based Cost Modeling
SE – Systems Engineering
SmallSat – Small Satellite
SMD – Science Mission Directorate
SME – Space Mission Engineering: The New SMAD
SSCM – Small Satellite Cost Model
SSTL – Surrey Satellites Technology Limited
RE – Recurring Engineering
R&D – Research and Development
TFU – Theoretical First Unit
13

TRL – Technology Readiness Level
USAF – United States Air Force
USCM – Unmanned Space Vehicle Cost Model

Constants
𝑔
!
= 9.8066 m/s
2

𝐾
!
= 2.55604187×10
!

𝑅
!
= 6378.1366 km
𝜇
!
= 3.986004356×10
!
km
3
/s
2

𝜋= 3.1415926

Variables (Greek Symbols)
𝛼 – Savings due to amortization
β – Ballistic coefficient
ΔV – Delta V
λ – Observation wavelength
𝜇 – Gravitational parameter
ρ – Local atmospheric density
θ – Resolution

14

Variables
𝑎 – Semi-major axis
𝐴 – Satellite cross-sectional area along the velocity vector
𝐴𝐴𝑅 – Area access rate
𝐴𝐴𝑅
!"#
– Required area access rate
𝐶 – Cost
𝐶
!
– Total amortization cost (with learning curve)
𝐶
!
– Satellite drag coefficient
𝐶
!"#$%!
- Launch cost per kilogram
𝐶
!
– Total remaining launch cost
𝐶
!
– Total remaining spacecraft production cost (with learning curve)
𝐶
!
– Total mission cost
𝐷 – Telescope aperture diameter
𝐷𝐹𝑈
!"#
– Aerospace ground equipment development plus first unit cost
𝐷𝐹𝑈
!"&!
– Spacecraft integration, assembly, and test development plus first unit cost
𝐷𝐹𝑈
!""#
– Launch operations and orbital support development plus first unit cost
𝐷𝐹𝑈
!"#"$%&%#'
– Program level development plus first unit cost
𝐷𝐹𝑈
!"#$
– NICM portion of development plus first unit cost
𝐷𝐹𝑈
!"#$%"&
– Payload development plus first unit cost
𝐷𝐹𝑈
!"
– Product assurance development plus first unit cost
𝐷𝐹𝑈
!"#$"%& !"#"$
– Program level development plus first unit cost
𝐷𝐹𝑈
!"
– Systems engineering development plus first unit cost
15

𝐷𝐹𝑈
!!"#
– SSCM portion of development plus first unit cost
𝐷𝐹𝑈
!"#$%$&#'( !"#
– Spacecraft bus development plus first unit cost
𝐸𝐶𝐴 – Earth central angle
𝐹
!"#
– Fraction of non-recurring engineering cost over total upfront cost
𝐹
!"
– Fraction of recurring engineering cost over total upfront cost
g
!
– Gravitational acceleration at the Earth’s surface
ℎ – Orbit altitude
𝑖 – Interest rate
𝐼
!"
– Specific impulse
𝑀 – Satellite mass
𝑀
!
– Spacecraft dry mass
𝑀
!
– Payload mass
𝑀
!
– Spacecraft launch mass
𝑀
!
– Propellant mass
𝑛 – Number of equal payments
𝑁 – Number of spacecraft
𝑁𝑅𝐸
!"#
– Aerospace ground equipment non-recurring engineering cost
𝑁𝑅𝐸
!"&!
– Spacecraft integration, assembly, and test non-recurring engineering cost
𝑁𝑅𝐸
!"#$
– Total NICM portion of non-recurring engineering cost
𝑁𝑅𝐸
!"#$%"&
– Payload non-recurring engineering cost
𝑁𝑅𝐸
!"#$"%& !"#"$
– Program level non-recurring engineering cost
𝑁𝑅𝐸
!"#$%$&#'( !"#
– Spacecraft bus non-recurring engineering cost
𝑁𝑅𝐸
!!"#
– Total SSCM portion of non-recurring engineering cost
16

𝑁𝑅𝐸
!"#$%
– Total non-recurring engineering cost
𝑁𝑅𝐸
!"#$
– Total USCM portion of non-recurring engineering cost
𝑃 – Power requirement
𝑟 – Distance of satellite from central body
𝑅 – Data rate
𝑅
!"#
– Reference data rate that meets the AAR requirement
𝑅𝐸
!"&!
– Spacecraft integration, assembly, and test recurring engineering cost
𝑅𝐸
!""#
– Launch operations and orbital support recurring engineering cost
𝑅𝐸
!"#$
– Total NICM portion of recurring engineering cost
𝑅𝐸
!"#$%"&
– Payload recurring engineering cost
𝑅𝐸
!"#!"#$ !"#"$
– Program level recurring engineering cost
𝑅𝐸
!"#$%$&#'( !"#
– Spacecraft bus recurring engineering cost
𝑅𝐸
!!"#
– Total SSCM portion of recurring engineering cost
𝑅𝐸
!"#$%
– Total recurring engineering cost
𝑅𝐸
!"#$
– Total USCM portion of recurring engineering cost
𝑆 – Learning curve
𝑇 – Orbit period
𝑇𝐹𝑈 – Theoretical first unit cost
𝑣 – Orbital velocity
𝑉 – Principal value

17

1 Introduction
At the start of the space program, spacecraft were small and capable of limited performance.
However, launch costs were very high which drove up mission costs. As a result, several
processes and requirements were put in place to ensure that the spacecraft, its subsystems, and
their parts had a long life span and high reliability (>99.9%). This measure led to parts
redundancy and increased testing. These, in turn, increased program schedules and further
increased costs. On top of that, spacecraft were required to last 5 – 15 years to justify high
mission costs. This meant that they had to be flown at high altitudes to circumvent the need to
add large amounts of propellant for orbit maintenance. Above approximately 500 km altitude, it
is relatively easy for a satellite to stay in a circular orbit above Earth for several years to several
hundred or even thousands of years [Wertz, et al. 2012]. In summary, the ever-increasing cost of
space missions led to longer schedules and fewer missions. Understandably, this led to a demand
for higher reliability, which, inevitable means higher cost, longer schedules, and fewer missions.
This cycle known as the “space spiral,” is shown in Figure 1-1. The James Webb Space
Telescope (JWST) for example, is a program that is currently facing this critical issue [U.S.
Government Accountability Office, 2012].

Figure 1-1. The Space Spiral [Wertz, Everett, and Puschell, 2011]
18

Over time, launch vehicles allowed for larger spacecraft, but spacecraft continued to be designed
to fly at higher altitude regimes (> 500 km) and extended periods of time. These “traditional”
large satellites have been in operation ever since their first use on a space program. They have
been highly instrumental in meeting the goals of NASA, DoD, and the intelligence community.
However, the country is at a point where there are more missions to accomplish, than there is
funding to accomplish them. Thus, methods capable of dramatically reducing space mission
costs are worth pursuing because the major problem facing space systems today is simple: it
costs too much to build and launch a spacecraft.
However, in recent times, small commercial startups companies such as Skybox Imaging and
Planet Labs have broken this trend and have been designing and flying smaller low-cost
spacecraft at lower altitudes to provide imaging capabilities worldwide. In light of the foregoing,
the goal of my research is to quantify the effect of orbit altitude on mission cost for Earth
observation satellites. My research is intended to help address the issue of high cost in an effort
to help the aerospace community reverse the space spiral by determining how mission cost can
be reduced for Earth observation satellites, while still meeting mission requirements. To continue
making technological advances in space, it is imperative to reverse the space spiral. Reversing
the space spiral means reducing the cost of space missions, reducing the pressure to ensure a
return on a large investment, and allow for programs to be developed in less time to respond to
needs for Earth observation, communication, navigation, national defense, and space exploration.
The method that I developed during my doctoral research, Performance-Based Cost Modeling
(PBCM), models the total mission costs vs. orbit altitude as a function of fixed performance
requirements, and is described in detail in Sec. 4. The advantage of PBCM is that it takes an
interdisciplinary approach that utilizes physics, systems engineering, the use of existing, well-
19

established, cost models, and incorporates common cost estimating techniques. My goal is not to
create a new cost model, but to use existing, accepted cost models in a new way. The completion
of this research is expected to positively impact the way business is done in space and effectively
start reversing the space spiral as shown in Figure 1-2.

Figure 1-2. Reversing the Space Spiral

20

2 Statement of the Problem
2.1. The Problem
Currently, there is a clear and present budget problem in DoD that needs to be addressed. Arati
Prabhakar, DARPA’s Director, was quoted in Space News
*
saying there is “something going on
inside the national security community in space that's actually quite troubling, that has to do with
how slow and costly it is for us today to do anything we need to do on orbit for national security
purposes.” The USAF has announced a series of studies to determine the future of its big satellite
programs. General Shelton was quoted in Space News
†
stating, “Do we want to continue with the
military dedicated constellation? Can we turn either a portion or all of this over to a commercial
provider and contract for a service?” To add context to these remarks, the commercial providers
Gen. Shelton refers to have offered the same technologies but at less cost. Mark Valerio, VP of
Lockheed Martin's Military Space business, quoted in Space News saying, “We’re looking at
innovative options for hosting payloads, and we are suggesting ways to reduce costs while
maintaining our technology edge to address evolving threats.” Google has also demonstrated that
the demand for low cost satellite imagery is high by announcing their purchase of Skybox
Imaging, a company that makes Earth imaging microsatellites for $500 million
‡
.
The sequestration in 2013 made it clear that government spending must be reduced. For the
space community, this means the space budget will either be cut or remain approximately the
same. One thing is for sure – the funding for the space industry will not increase to where the
U.S. would like any time soon. The United States has more missions that need to get done than
there is time and money available to do them. If the U.S. continues with the traditional way of

*
Space News. 2014. “DARPA Chief Says Space Programs Are Too Slow and Costly.” January 15.
†
Space News. 2014. “Thule Air Base USAF Examining Alternative for All of Its Big Satellite Programs.” February 17.
‡
Space News. 2014. “Google to Buy SkyBox for $500 Million.” June 10.
21

doing business, there is the potential of physical gaps between missions that need continuity,
such as weather and climate data, and surveillance. Additionally, the U.S. does not have launch
on demand capability. Without dedicated launch vehicles, it is impossible for the U.S. to respond
in a timely fashion to emergencies when they arise. This is a capability that Russia (and
previously, the Soviet Union) has had for the past four decades; thus, it is clearly possible
[Cooper, 1992]. The bottom line is that the cost of space missions in the U.S. is unaffordably
high.
A problem that U.S. space programs are facing is the fact that we do not clearly understand how
orbit altitude effects space mission cost specifically for Earth observation satellites. Some of the
Earth observation satellites today are designed to last 10 to 15 years in high orbits in LEO (> 500
km), such as GeoEye-2
*
and Worldview-2
†
. On the other hand, Planet Labs
‡
and Skybox
§
are
designing shorter-lived Earth observation satellites to fly in lower orbits in LEO (< 500 km). All
these missions have differences when it comes to characteristics such as, resolution, coverage
rate, design-life, and cost. However, given a fixed set of performance requirements, what orbit
altitude will provide the lowest overall mission cost? What is the relationship between mission
cost and orbit altitude? Without a careful analysis, it is difficult to determine these answers
because there are so many variables to consider. This research has answered these questions.
Traditional cost models for space systems are typically weight-based, primarily because mass is
determined or assigned early in mission design and has historically correlated well with actual
hardware cost. Some commonly used cost models are the Unmanned Space Vehicle Cost Model

*
GeoEye. 2012. GeoEye-2 Fact Sheet, Rev 03/12.
†
Digital Globe. 2013b. Worldview-2 Datasheet. DS-WV 06/13.
‡
MIT Technology Review. 2013. “Startup Plans Constellation of Tiny Monitoring Satellites.” June 27.
§
Skybox Imaging. 2010. “SkySat-1 Approved for Takeoff.” April, 20. Website: http://www.skyboximaging.com/blog/skysat-1-
approved-for-takeoff
22

(USCM), the Aerospace Corporation Small Satellite Cost Model (SSCM), and the NASA
Instrument Cost Model (NICM). Unfortunately, none of the existing cost models provide direct
insight on how to reduce space mission cost. In addition, most acquisition performance analyses
focus on cost overruns, or how much the system costs relative to what it is expected to cost. The
research reported builds on what the cost modeling community has done so far and analyzed
different mission scenarios for Earth observation to find a quantifiable relationship between
mission cost and orbit altitude.

2.2. Research Objectives
The goals of this research are as follows:
1. Develop a systems engineering approach to quantify the relationship between mission
costs vs. orbit altitude for Earth observation missions given a fixed level of performance
(resolution, coverage, and lifetime).
2. Find preliminary results and formulate a research hypothesis with respect to reducing
space mission cost.
3. Present approach and preliminary research at conferences and workshops to introduce the
concept to the aerospace and cost modeling community to gain feedback and peer review.
4. Identify sets of realistic performance requirements for Earth observation missions and
test hypothesis on each case using formulated approach.
5. Analyze and evaluate the results collectively to validate (or disprove) the hypothesis, and
determine any additional conclusions useful and beneficial to the aerospace community.
23

3 Literature Review
3.1. Current Approaches to Cost Reduction
The most predominant current approach to controlling cost on space programs is to monitor and
try to reduce “cost overruns.” That is, cost in excess of the original budget, which is typically
the primary concern for government acquisition. This is a management problem associated with
cost performance relative to expectations and relative to the total amount of money available for
a set of tasks. They are also a problem for the contractor since overruns erode their credibility
and may reduce the available fee. This problem is most easily resolved by simply reducing
expectations. For example, if we originally planned to buy 100 airplanes for $10 billion, but
changes in the system have made each plane more expensive, then the easiest solution is to
simply reduce the number of airplanes to, say 75, to keep the budget at $10 billion. Both the
contracting process and contractor are satisfied and the cost overrun disappears.
The problem, of course, comes when trying to meet the needs of the end user. In our airplane
example, we were buying the planes to accomplish a set of missions, presumably with a few
extra planes to account for maintenance and downtime. But now we have only three-quarters as
many planes as we needed. This means fewer missions can be accomplished, either because of
the smaller number of planes or because we had to divert resources from some other activity to
buy the additional 25 planes. From the point of view of the end user, it isn’t the management
problem of cost overruns that is important, but rather the problem of how much performance we
can achieve for how much money. A second distinction arises depending on whether the
program is an operational activity or an R&D activity. For operational programs, such as GEO
communications satellites, cost overruns are both important and bad. For these systems, cost
24

should be well understood and well controlled. However, for Research & Development (R&D)
programs some amount of cost overrun should be acceptable and expected. If there are never any
overruns in R&D programs, then we’re clearly not pushing hard enough on cost reduction, and
we should make our cost goals more aggressive.
The U.S. Government Accountability Office [2012] released a report, “Actions Needed to
Improve Cost Estimate and Oversight of Test and Integration” for the James Webb Space
Telescope (JWST) that addresses the issue of cost and schedule overruns. This approach, for
example, is inadequate in reversing the space spiral because it does not address the underlying
issues that cause high costs in the first place. Many of these underlying causes have been
identified anecdotally, and the solutions have been identified by aerospace professionals and
documented as “best practices” or “lessons learned.” However, the cost savings of implementing
these methods of reducing space mission costs have not been quantified, and as a result, the
motivation for implementing these methods has been lost. There are many studies suggesting
methods and strategies for reducing space mission costs that are based on anecdotal evidence;
however, quantitative evidence for which constellation design will cost the least based on a set of
mission requirements has not yet been developed.
For the purpose of creating much lower cost, high utility missions, cost (and schedule) for a
given level of performance should be our measure of success, not whether cost overruns occur.
This alternate approach is a major purpose of performing this research. Reducing Space Mission
Cost by Wertz and Larson [1996] provide process changes to reduce cost and various case
studies.

25

3.2. Cost Models
Cost estimation is an empirical process of calculating the expected cost of a space mission. The
first step is to establish the estimating ground-rules and assumptions, which can be extracted
from the mission description and the development plan. Ground-rules are statements about the
form and content of the estimate, and they establish the scope of the estimate, distinguishing
specifically between cost that are included and those excluded from the analysis. Assumptions
are suppositions about what will happen at some future time. They help identify significant cost
drivers and indirectly the choice of a parametric estimating cost model. [Apgar, 2011]
Parametric estimates, also known as “top-down” estimates, statistical cost analysis estimates,
cost analysis estimates, formula estimates, cost-to-cost estimates, or CER estimates, are based on
mathematical expressions relating cost as the dependent variable to selected, independent, cost-
driving variables for the component being costed [The National Estimating Society, 1982]. The
parametric cost estimating process is supported by cost estimating tools including cost estimating
models and normalized historic databases. Models are comprised of Cost Estimating
Relationships (CERs), which are statistically-based cost-predicting algorithms derived from
these databases. CERs relate cost to one or more cost drivers, such as weight, power, and amount
of additional design required. [Apgar, 2011]
Parametric methods can range from high-level, one-CER approaches, such as dollars-per-pound
“rules of thumb,” to lower level, multiple CER approaches. Models using a parametric approach
include the Unmanned Space Vehicle Cost Model, NASA/Air Force Cost Model (NAFCOM),
NASA Instrument Cost Model (NICM), Aerospace Corporation Small Satellite Cost Model
(SSCM), and the Office of the Secretary of Defense (OSD) Spacecraft Bus Model. [Tecolote
26

Research, 2015] Sections 3.2.1 through 0 will provide examples of select cost models that
currently exist and are widely used in the aerospace community today.
Figure 3-1 shows the basic characteristics of credible cost estimates. The basic characteristics of
effective estimating have been studied and highlighted many times. These characteristics are
validated by the U.S. Government Accountability Office (GAO) and should be found in all
sound cost analyses [U.S. Government Accountable Office, 2009]. The Unmanned Space
Vehicle Cost Model (USCM), the NASA Instrument Cost Model (NICM), and the Small
Satellite Cost Model all meet these key factors and are introduced in Secs. 3.2.1 – 3.2.3.

Figure 3-1. Characteristics of a Cost Model [U.S. Government Accountable Office, 2009]
27

3.2.1. Unmanned Space Vehicle Cost Model (USCM)
The Unmanned Space Vehicle Cost Model, Version 8 (USCM), was developed by Tecolote
Research for the US Air Force, Space and Missile Systems Center [Tecolote Research, 2002] and
is currently published in Space Mission Engineering: The New SMAD (SME) [Wertz, Everett,
and Puschell, 2011]. The CERs were derived using statistical regression techniques from 44
satellites to support parametric cost estimates of unmanned, Earth-orbiting space vehicles. Out of
the 44 satellites, 23 are military, 12 are from NASA, and 9 are commercial. USCM provides cost
estimates for both non-recurring and recurring engineering costs. The model estimates are for
contractor and subcontractor cost including fee (cost to the government) but does not include
government program office costs.
Since its initial publication, USCM has been recognized throughout the aerospace industry as the
most widely applied space vehicle cost estimating tool with the broadest database available.
Since being published in 2011 in SME, the USCM cost model has been updated to Version 9 and
it is now online where it is constantly being updated. However, this is not available for public
release. [Tecolote Research, 2015]. Table 3-1 provides the non-recurring CERs from USCM, and
Table 3-2 provides the recurring CERS from USCM, both taken from Apgar [2011].

28

Table 3-1. Unmanned Space Vehicle Cost Model Non-Recurring Engineering CERs [Apgar, 2011]
SME-SMAD WBS Element
(Non-recurring subsystem)
CER
Y = non-recurring cost in
FY2010 thousands of
dollars for development
plus one qualification unit. Cost Driver(s)
Cost Driver Input
Range SEE
1.1 Spacecraft
1.1 Spacecraft Bus (alternate
CER when no component
information is available)
Y = 108 X1 X1 = Spacecraft Weight (kg) 114–5,127 kg 47%
1.1.1/1.1.2 Structure and
Thermal Control
Y = 646 X1
0.684
X1 = Structure + Thermal
Weight (kg)
59–501 kg 22%
1.1.3 Attitude Determination &
Control System (ADCS)
Y = 324 X1 X1 = ADCS Weight (kg) 35–524 kg 44%
1.1.4 Electrical Power System
(EPS)
Y = 64.3 X1 X1 = EPS Weight (kg) 47–1,065 kg 41%
1.1.5 Propulsion (Reaction
Control)
Y = 20.0 X1
0.485
X1 = Total RCS tank volume
(cubic centimeters)
Not given 35%
1.1.6 Telemetry, Tracking, &
Command (TT&C)
Y = 26,916 Y = Average TT&C Cost
(since there is no statistical
CER for this element)
CER based on S-
Band telemetry
Not
given
1.2 Payload
1.2 Communications Payload
(based on weight and
number of channels)
Y = 339 X1 + 5,127 X2 X1 = Communications
Subsystem Weight (kg)
X2 = Number of
Communication Channels
160–395 kg

2–32 channels
40%
1.2 Communications Payload
(alternate CER based on
weight alone)
Y = 618 X1 X1 = Communications
Subsystem Weight (kg)
160–395 kg 38%
1.3 Spacecraft Integration, Assembly, and Test
1.3 Integration, Assembly, &
Test (of bus and payload
into spacecraft)
Y = 0.195 X1 X1 = Spacecraft Bus +
Payload Non-recurring Cost
($K)
3,600–545,000 $K 42%
4.0 Program Level
4.0 Program Level (for a
Communications Satellite)
Y = 0.236 X1 X1 = Space Vehicle and
IA&T Non-recurring Cost
($K)
7,850–353,804 $K 23%
4.0 Program Level (for an other
than Communications
Satellite)
Y = 0.357 X1 X1 = Space Vehicle and
IA&T Non-recurring Cost
($K)
7,850–353,804 $K 50%
6.0 Aerospace Ground Equipment (AGE)
6.0 Aerospace Ground
Equipment (AGE)
Y = 0.432 X1
0.907
× 2.244
X2
X1 = Spacecraft Bus
Non-recurring Cost ($K);
X2 = 0 for comm sats and
X2 = 1 for non-comm sats
7,850–353,804 $K 37%

29

Table 3-2. Unmanned Space Vehicle Cost Model Recurring Engineering CERs [Apgar, 2011]
SME-SMAD WBS Element
(Recurring subsystem T1)
CER
Y = Recurring T1 cost in
FY2010 thousands of
dollars Cost Drivers
Cost Driver Input
Range SEE
1.1 Spacecraft
1.1 Spacecraft Bus (alternate CER
when no component
information is available)
Y = 283.5 X1
0.716
X1 = Spacecraft Weight (kg) 288–7,398 kg 21%
1.1.1/1.1.2 Structure and Thermal
Control (a)
Y = 22.6 X1 X1 = Structure + Thermal
Weight (kg)
59–501 kg 21%
1.1.3 Attitude Determination &
Control System (ADCS)
Y = 795 X1
0.593
X1 = ADCS Weight (kg) 27–524 kg 36%
1.1.4 Electrical Power Supply
(EPS)
Y = 32.4 X1 X1 = EPS Weight (kg) 111–1,479 kg 31%
1.1.5 Propulsion Apogee Kick
Motor (AKM)
Y = 29 X1 + 0.024 X2 X1 = AKM Weight (kg)
X2 = Burn-time (seconds)
81–966 kg 22%
1.1.6 Telemetry, Tracking, &
Command (TT&C)
Y = 883.7 X1
0.491
× 1.13
X2
X1 = TT&C weight (kg)
X2 = Geosynchronous Transfer
Orbit (1 = yes; 0 = no)
12–76 kg for S-band 18%
1.2 Payload
1.2 Communications Payload Y = 189 X1 X1 = Communications Payload
Weight (kg)
38–928 kg 39%
1.3 Spacecraft Integration, Assembly, and Test
1.3 Integration, Assembly, & Test
(IA&T) of bus and payload into
space vehicle
Y = 0.124 X1 X1 = Spacecraft Bus + Payload
Recurring Cost ($K)
35,367–142,044 $K 34%
4.0 Program Level
4.0 Program Level (for a
Communication Satellite)
Y = 0.234 X1 X1 = Space Vehicle
(Spacecraft Bus + Payload +
IA&T) Recurring Cost ($K)
13,287–268,225 $K 12%
4. Program Level (for an other
than communication satellite)
Y = 0.320 X1 X1 = Spacecraft (Spacecraft
Bus + Payload + IA&T)
Recurring Cost ($K)
13,287–268,225 $K 40%
5.0 Flight Support
5.0 Launch Operations & Orbital
Support (LOOS)
Y = 5,850 Y= Average LOOS cost in $K Not given Not
given

3.2.2. NASA Instrument Cost Model (NICM)
The NASA Instrument Cost Model (Version IIIC) was developed by the Jet Propulsion
Laboratory in 2010 from 159 data points obtained from instrument contractors [Habib-agahi,
2010]. NICM is a probabilistic cost estimation tool that contains a system model, a subsystem
30

model, and a database search engine. NICM is a proven NASA cost estimation tool that
enhances current instrument cost modeling capabilities by providing cost estimates at both the
system and subsystem level. NICM is in wide use across many NASA centers and is available
under access release restrictions to external organizations
*
. This model predicts the cost of
development plus one flight unit, also known as the theoretical first unit (TFU). The model
predicts the cost of development plus one flight unit of each of five payload instrument types
shown in Table 3-3. These costs include contractor fees. However, NICM has separate CERs for
management, systems engineering, product assurance, and integration & test. These are called
“wrap” factors and are listed in Table 3-4.
Table 3-3. NASA Instrument Cost Model CERs [Apgar, 2011]

CER
Y in FY2010 thousands of dollars for
development plus production of one
protoflight unit Cost Drivers [plus Data Range]
1.2.2 Optical
Planetary (for instruments
visiting planets other than
Earth) Payload (e.g.
cameras, spectrometers,
interferometers)
Y = 328 × M
0.426
× P
0.414
× DL
0.375
R
2
= 0.76
SEE = 39%
M = Instrument Total Mass in kilograms (kg). [1–75 kg]
P = Maximum Instrument Power in watts (W). [1–75 W]
DL = Design Life in months [10–150 months]
1.2.2 Optical
Earth-Orbiting (instruments
on spacecraft in geocentric
orbits) Payload (e.g.,
cameras, spectrometers,
interferometers)
Y = 1,163 × M
0.328
× P
0.357
× DR
0.092
R
2
= 0.89
SEE = 35%
M = Instrument Total Mass in kilograms (kg). [10–350 kg]
P = Maximum Instrument Power in watts (W) [0. 5–400 W]
DR = Total Data Rate in kilobits per second (kbps). [0.1–
30,000 kbps]
1.2.2 Active and Passive
Microwave Payload (e.g.,
radars, altimeters, scatter
meters, sounders, GPS
receivers)
Y = 23,620 × M
0.284
× P
0.325
× DR
0.090
× T
–
1.296
R
2
= 0.88
SEE = 37%
M = Instrument Total Mass in kilograms (kg). [10–50 kg]
P = Maximum Instrument Power in watts (W). [10–600 W]
DR = Total Data Rate in kilobits per second (kbps). [0.1–
1,000 kbps]
T = Instrument Technology Readiness Level (TRL) [4–9]
1.2.2 Particles Payload
(e.g., plasma detectors,
plasma wave detectors)
Y = 980 × M
0.327
× P
0.525
× DL
0.171
R
2
= 0.65
SEE = 29%
M = Instrument Total Mass in kilograms (kg). [1–40 kg]
P = Maximum Instrument Power in watts (W). [1–40 W]
DL = Design Life in months. [10–150 months]
1.2.2 Fields Payload (e.g.,
electric field detectors,
magnetic field detectors)
Y = 1,130 × M
0.184
× P
0.238
× DL
0.274
R
2
= 0.87
SEE = 28%
M = Instrument Total Mass in kilograms (kg). [0.1–35 kg]
P = Maximum Instrument Power in watts (W). [0.1–25 W]
DL = Design Life in months. [1–100 months]

*
NASA. 2015a. NASA Instrument Cost Model – NICM. Website: http://www.nasa.gov/content/nicm/#.VQjYdNKjPTo
31

Table 3-4. Estimated Wrap Factors CERs for NASA Instrument Cost Model [Apgar, 2011]
SMAD WBS Element
CER
Y in FY2010 thousands of dollars for
program-level cost elements Cost Drivers
4.2 Management Y = 0.07124 × S
1.0317
R
2
= 0.85.
S = Sensor Cost from CER in Table 3-3
4.1 Systems Engineering Y = 0.4931 × S
0.8645
R
2
= 0.75
S = Sensor Cost from CER in Table 3-3
4.4 Product Assurance Y = 0.1427 × S
0.9422
R
2
= 0.91
S = Sensor Cost from CER in Table 3-3
4.3 Integration and Test (I&T) Y = 0.1457 × S
1.0071
R
2
= 0.87
S = Sensor Cost from CER in Table 3-3

3.2.3. Small Satellite Cost Model (SSCM)
The Small Satellite Cost Model (SSCM) is developed by The Aerospace Corporation [1996].
This is a parametric cost model for predicting development and TFU cost for smaller Earth-
orbiting and near-planetary spacecraft. The CERs are based on 53 individual satellites, most of
which are below 100 kg. SSCM employs a parametric methodology for estimation of program
cost, and is best suited to the early, conceptual development phase of a spacecraft program,
during which time the design is likely to be less mature, and when cost and performance trades
can be easily performed. SSCM consists of a collection of cost-estimating relationships, or
CERs, which estimate the costs of developing and producing a spacecraft system.

32

Table 3-5. Small Spacecraft Cost Model CERs [Apgar, 2011]
SME-SMAD WBS Element
CER
Y = total non-recurring
cost of development plus
one protoflight flight unit in
FY10 $K Cost Driver(s)
Cost Driver
Input Range
Standard
Error of
Estimate
(absolute)
FY10 $
1.1 Spacecraft
1.1 Spacecraft Bus (alternate
CER when no component
information is available)
Y = 1,064 + 35.5 X
1.261
X = Spacecraft Bus Dry Weight
(kg)
20–400 kg 3,696
1.1.1 Structure Y = 407 + 19.3 X × In(X) X = Structure Weight (kg) 5–100 kg 1,097
1.1.2 Thermal Control Y = 335 + 5.7 X
2
X = Thermal Control Weight (kg) 5–12 kg 119
1.1.3 Attitude Determination &
Control System (ADCS)
Y = 1,850 + 11.7 X
2
X = ADCS Dry Weight (kg) 1–25 kg 1,113
1.1.4 Electrical Power Supply
(EPS)
Y = 1,261 + 539 X
0.72
X = EPS Weight (kg) 7–70 kg 910
1.1.5 Propulsion (Reaction
Control)
Y = 89 + 3.0 X
1.261
X = Spacecraft Bus Dry Weight
(kg)
20–400 kg 310
1.1.6a Telemetry, Tracking, &
Command (TT&C)
Y = 486 + 55.5 X
1.35
X = TT&C Weight (kg) 3–30 kg 629
1.1.6b Command & Data
Handling (CD&H)
Y = 658 + 75 X
1.35
X = Command & Data Handling
Weight (kg)
3–30 kg 854
1.2 Payload
1.2 Payload Y = 0.4 X X = Spacecraft Bus Total Cost ($K) 2,600–69,000
($K)

1.3 Spacecraft Integration, Assembly, and Test
1.3 Integration, Assembly, &
Test (IA&T)
Y =0.139 X X = Spacecraft Bus Total Cost ($K) 2,600–69,000
($K)

4.0 Program Level
4.0 Program Level Y = 0.229 X X = Spacecraft Bus Total Cost ($K) 2,600–69,000
($K)

5.0 Flight Support
5.0 Launch & Orbital
Operations Support (LOOS)
Y = 0.061 X
X = Spacecraft Bus Total Cost ($K)
2,600–69,000
($K)

6.0 Aerospace Ground Equipment
6.0 Ground Support Equipment
(GSE)
Y = 0.066 X
X = Spacecraft Bus Total Cost ($K)
2,600–69,000
($K)

Its development was motivated by the observation that traditional cost models, based on larger
civil and military space systems, tended to drastically over predict the development costs of
modern, smaller satellites (post 1990 and under 1000 kg). The SSCM was initiated in 1989, and
remains one of the most relevant and credible cost models today for performing cost estimates of
small spacecraft. In general, a new version of the SSCM is developed whenever a significant
33

number of new missions are added to the SSCM database. Currently, the model is planned to be
released approximately every two years. This ensures that the model properly reflects the latest
trends in cost-efficiency and technology development, with the most up-to-date database of
spacecraft buses.
*
Table 3-5 provides the CERs for SSCM from 1996 published in Wertz,
Everett, and Puschell [2011]. More recent versions are not available for public release.
3.2.4. Cost Model Limitations
Cost models are a great tool to project program cost early in mission design. USCM, NICM,
SSCM, and several other cost models all have one thing in common – they all use CERs to
estimate costs. However, there are some limitations to using cost models. Other than reducing
size, weight, power, or other characteristics CERs use to predict cost, cost models cannot tell you
how to change your design to reduce cost. There are no known cost models that incorporate
performance as a parameter to determine cost vs. performance. In other words, existing cost
models do not have cost versus performance tradeoffs. There is no way to vary the altitude in
which you fly to project costs. However, if used correctly, cost models can be used as a tool to
estimate cost over a range of parameters, and thus provide a means for finding a relationship
between cost and performance or orbit altitude.

3.3. Altitude of Spacecraft
There are many different orbits around Earth that spacecraft can fly, such as, Lower Earth Orbit
(LEO), Medium Earth Orbit (MEO), Geosynchronous Orbit (GEO), and Highly Elliptical Orbits
(HEO). For Earth observation missions, spacecraft will typically fly in LEO in order to take

*
Aerospace Corporation. 2015. SSCM Overview. Website: http://www.aerospace.org/expertise/technical-resources/small-
satellite-cost-model/sscm-overview/
34

advantage of diffraction-limited optics for higher resolution imaging, so the focus will be on
altitudes in LEO. For Earth observation, there are many advantages to flying spacecraft at higher
altitudes, and many advantages to flying spacecraft at lower altitudes. In this study, I will refer to
higher altitudes as being greater than 500 km in LEO. Lower altitudes will be altitudes less than
(or equal to) 500 km in LEO. Section 3.2.1 discusses the advantages of flying at higher altitudes,
and Sec. 3.3.2 provides advantages of flying at lower altitudes. In Sec. 3.3.3, I will discuss the
relationship between cost vs. altitude for Earth observation satellites.
3.3.1. Advantages of Higher Altitudes
Most spacecraft flown in LEO today are above 500 km. Although LEO extends all the way to
about 2,000 km, most spacecraft only orbit between 500 and 1,000 km. This is due to the fact
that the Van Allen radiation belts come down to about 1,000 km and can damage and destroy
spacecraft with its harsh radiation environment [Tobiska and Sarzi-Amade, 2011].
There are many other reasons why most spacecraft orbit between 500 and 1,000 km. The main
reason is due to the fact that the density of the atmosphere drops off exponentially with altitude
[Wertz, Everett, and Puschell, 2011] and there are many benefits to being in a low-density
atmosphere. There are less on-orbit maneuvers you need to make in order to sustain the desired
orbit because there is less of a drag environment. This means less propellant and less mass
needed on orbit, potentially allowing room for more hardware. Another benefit to a low-density
atmosphere is that it allows you to fly in orbit for a relatively long period of time because there is
less drag. Without a strong drag environment, a spacecraft can fly indefinitely and therefore can
allow for a relatively long design life.
35

Another advantage to flying at higher altitudes is that you can see or access more land. The field
of regard (FoR) is greater at higher altitudes. The advantage of this is that you have the ability to
“see” more land at any moment, given that the spacecraft is able to point in that direction. For
Earth observations, this will allow you to image more locations on Earth at any given time.
Communicating with various ground stations can be more frequent as well, although
communication satellites are typically flown in GEO. This is a major advantage over spacecraft
flown in lower altitudes where the FoR is less.
3.3.2. Advantages of Lower Altitudes
Flying at lower altitudes may be advantageous for Earth observation satellites, although, it does
not apply to every single mission. Below is a list of advantages for flying a satellite at lower
altitudes from Stuart Eves [2013].
1. If the resolution of the required system is already adequate, a reduction in orbit altitude
allows a smaller, cheaper, and lighter sensor to be used. Optical systems greater than 1
meter in diameter can weigh as little as 10 kg manufactured from new, lightweight
materials [Baker and Worden, 2008].
2. For a given (passive or active) imaging sensor, the resolution or performance improves
proportionally as you lower the altitude. Applying Rayleigh’s criterion, for a given
aperture size, the nearer the satellite is to its target, the better the resolution that can be
achieved. And geolocation accuracy will also improve.
3. For a given aperture size, the effective surveillance footprint size of the mission actually
increases as the orbit altitude decreases, so the timeliness of revisit is better. The
36

maximum slant range, (i.e. the maximum range at which the resolution criterion for the
mission is satisfied), is unchanged. Figure 3-2 shows this concept.
4. The lower the satellite orbit, the greater the mass of hardware and/or payload that can be
placed into orbit for a given launch vehicle (Figure 3-3).
5. A shorter path length makes it easier to establish an adequate communications link
budget to a terminal on the ground. This could be a particular advantage if future satellite
communication terminals are smaller, lower power, and less cooperative. A shorter path
length is also a potential advantage for passive signals surveillance missions.
6. Flying lower permits the collection of unique data sets that would not otherwise be
possible (e.g., the gravity map resolution of the GRACE mission [Tapley, et al. 2004]).
7. There is no need to perform de-orbit maneuvers since atmospheric drag can bring the
satellite down “for free.” And the problem of long-term orbital debris environment is
mitigated since spacecraft below approximately 500 km will decay within a few days to
several months. Figure 3-4 shows the spatial density of orbital debris particles in LEO.

Figure 3-2. Effective Surveillance Footprint Sizes [Eves, 2013]
37

Figure 3-3. Spacecraft Mass Available to Launch vs. Orbit Altitude for a Fixed Launch System,
where H
π
Represents the Altitude of Apogee [Eves, 2013]
38

Figure 3-4. Spatial Density of Orbital Debris Particles in LEO [Wertz, Everett, and Puschell, 2011]

Figure 3-5. Monthly Effective Mass of Objects in Earth Orbit by Region [NASA, 2015b]
39

3.3.3. Mission Cost vs. Orbit Altitude
It takes energy to place something into orbit, and it takes more energy the higher the orbit you
want to achieve for your spacecraft. For example, approximately 5 kg must be launched into
LEO for every kilogram that ultimately arrives in geosynchronous orbit (GEO) [Wertz, 2009].
Therefore, it is more expensive to launch a spacecraft into a higher altitude than it is to launch
the (same) spacecraft into a lower altitude, even if they’re both in LEO. There are two ways to
look at this; whether (1) your spacecraft is fixed, or (2) your launch vehicle is fixed. If you have
a particular spacecraft that you need to place into orbit, you may have several options for launch
vehicles. It is obvious that one would choose the lowest cost launch vehicle to get the job done.
But the problem is looking at which launch vehicle will get you to the altitude you need. Smaller
(and less expensive) launch vehicles can get you to LEO, but to get to a higher orbit, you may
need a larger (more expensive) launch vehicle. The altitude in which you desire to fly will
greatly impact the total mission cost. On the other hand, if the launch vehicle is fixed, then it is
predetermined how much weight you can put into each particular orbit. For example the SpaceX
Falcon 9 rocket
*
can place 13,150 kg into LEO, or 4,850 into a Geosynchronous Transfer Orbit
(GTO), or approximately 2,750 kg into Geosynchronous orbit. Therefore, the launch vehicle
chosen can greatly impact the design of the spacecraft, in particular the weight, and thus will
impact the overall cost of the spacecraft.
To my knowledge, there haven’t been any studies done that match the relationship between
mission cost and orbit altitude. It is expected that the total mission cost will increase as the orbit
altitude increases, simply by what was mentioned above about launch considerations. However,
it is difficult to make this assessment when you’re trying to compare missions with the same

*
SpaceX. 2015. “Falcon 9.” Website: http://www.spacex.com/falcon9
40

goals and requirements. For example, missions in LEO will tend to be for Earth observation and
remote sensing, whereas missions in GEO are more commonly communication satellites or GPS
constellations. Even within LEO, there are various types of missions with varying performance
requirements such as panchromatic vs. monochromatic, or high vs. low-resolution imagery.
Orbit altitude also has a major impact on resolution, coverage, and propellant requirements. For
specific resolution requirements, the orbit altitude has a major effect on the cost of the system.
For example, as you increase the orbit altitude, the size of the instrument must be increased to
meet the resolution requirement, thus increasing the cost of the payload (Sec. 3.3.2). This in turn
can increase the size and cost of the spacecraft bus to support the payload as well. Coverage is
another factor that is affected by orbit altitude. The higher the satellite orbit, the greater the field
of regard as mentioned in Sec. 3.3.1. If there is a coverage requirement, one can remain at low
altitude and just increase the number of satellites, or increase the altitude to meet the coverage
requirement. Both options can affect the final cost of the system. As mentioned in Sec. 3.3.1, the
drag environment is more dominant at lower altitudes, and therefore can affect the propellant
requirements for satellites at low altitudes. Delta V requirements can affect the design, size, and
cost of the spacecraft.

3.4. Small vs. Large Satellites
Space systems are often purchased with no knowledge of what historical examples may be
relevant. For example, it is often assumed that the approach embodied by large satellites is the
best way to perform a mission without knowing whether that mission has ever been done with
small satellites. [Bille, Kane, and Cox, 1998]. Table 3-6 summarizes the key differences between
41

small satellites and large satellites. The rest of this section will go into more detail comparing
advantages of both small and large satellites, in terms of schedule, risk, reliability, and cost.
Table 3-6. Difference Between Small Satellites and Large Satellites

3.4.1. Comparison of Advantages
Small Satellites
There is the perception that small satellites (SmallSats) are inherently much lower cost than more
traditional, larger satellites and can play a central role in reducing overall mission cost, but this
effect has been difficult to quantify. Without quantifiable evidence of their value, small satellites
have the potential to be under-utilized as a method for reducing space mission costs. Qualitative
analyses have been done to present the major advantages of SmallSats and have been convincing
to a certain extent. However, there have not been any known quantitative analyses done, to show
the cost reduction potential for Earth observation satellites. There is also major pushback with
SmallSats and low cost satellites within the aerospace community from those who tend to be
strong believers in traditional ways of doing business. Therefore, the United States continues to
use traditional large satellite since they have more flight heritage and they are known to work.
Nevertheless, space systems today still have the problem of costing too much.
Compared to traditional large satellites, there are many advantages to small satellites. The most
important of these are listed in Table 3-7. Resiliency is the ability of a system to circumvent,
Property Small Satellites Large Satellites
Mass < 500 kg > 500 kg
Design Life Typically 1-4 years Typically 5-15 years
Agility High Moderate
Redundancy Typically single-string Typically redundant
Reliability Good Mission Reliability Good System Reliability
Development Schedule 1-3 years 5-15 years
Cost $0.05M–$20M $50M–$2,000M
42

survive, and recover from failures to ultimately achieve mission objectives [Madni, 2012].
Pawlikowski, Loverro, and Cristler [2012] discuss new strategies that involve small satellites,
especially disaggregated spacecraft that supports more flexible and resilient systems needed by
DoD. The logical imperative for small satellites is that cost-effective SmallSats can,
simultaneously and synergistically, satisfy evolving national requirements for rapidly responsive
augmentation and reconstitution of space systems, and military requirements for assured access
to space capabilities in support of their operational forces [Bonometti and Nicastri, 1989].
Because small satellites are typically lower cost and have shorter lifetimes, they have the ability
to respond to new technologies within a few years and can meet the changing needs of the world
compared to large expensive satellites that have long lifetimes. Today’s strategic context
demands that the Department of Defense undertake actions that are swift, bold, and specific, and
the business model for small satellites—based on a bottom-up approach operationally,
technically, and financially—clearly meets the criteria [Cebrowski and Raymond, 2005]. And
due to their small size and smaller moments of inertia, small satellites are more agile and about
to maneuver in space more easily with less propellant.
Table 3-7. Advantages of Small Satellites
Advantage References
Ability to fly at lower altitudes with smaller
sensors
Sec. 3.3.1, Eves [2013]
Much lower cost Sec. 3.4.3, Aerospace Corp. [1996]
Shorter development schedules Sec. 3.4.2, Mosher [1999], Baker and Worden [2008]
Lower implementation and operations risk Sec. 3.4.2
More flexible and supports resilient and
responsive space
Sec. 3.4.1, DoD [2013b], Pawlikowski, Loverro, and
Cristler. [2012]
More responsive to new technologies and
changing needs
Sec. 3.4.1
More sustainable business model Cebrowski and Raymond [2005]
Greater agility in space Sec. 3.4.1
Support augmentation and reconstitution Sec. 3.4.1, Bonometti and Nicastri [1989]

43

SmallSats have the potential for major advantages in terms of mission utility. What I mean by
mission utility is how well a system does what it is intended to do. Figures of Merit (FoMs) or
Measures of Effectiveness (MoEs) quantify how well the mission meets its broad, overall
objectives. The most important MoEs typically relate to saving lives, property, or money, or
generating new knowledge that didn’t exist before. MoEs will depend on the objectives of each
individual mission. Examples include:
• How many lives or how much property was saved by an emergency response system?
• How well are we able to monitor and track activities in areas of interest?
• Was the war won because of the information provided by the system?
• Was the cost of the war (in both lives and dollars) reduced?
• Is the satellite commandable by the user in the field, with data returned to the user in
near-real-time?
Our objective should be to design a system that achieves a broad level of mission utility at very
low cost, and be able to achieve the best possible resolution, have as much delta V as possible,
and rapid slewing to cover diverse areas. Like any system, we want high value at low cost. The
benefits of high utility SmallSats include:
• Ability to respond to changing technology, even with on-the-shelf spacecraft
• Having smaller moments of inertia, allowing for potential greater agility (e.g., faster scan
rate)
• Lower cost per satellite and high agility provides many observing opportunities
• Ability to build low-cost systems to inventory and become available for launch-on-
demand
• Highly flexible design with slight impact on cost
44

• Ability to meet the needs of diverse users and accommodate multiple payloads
• Responsive systems have much higher level of utility, as well as being lower cost
In order to fully take advantage of all these great benefits, there needs to be access to space. A
key to a successful, viable small-satellite program is low-cost, frequent access to space. The lack
of such access is the biggest impediment to reestablishing a vigorous small satellite program.
[Baker and Worden, 2008]
Large Satellites
Since the start of the space program, large satellites have flooded the skies. These programs have
been dramatically successful. They are usually, though not always, physically large (but not
overweight) spacecraft characterized by having multiple payloads, a long mission lifetime, and
the requirement to obey all of the most stringent rules and requirements of long-lived, expensive
spacecraft, such as NASA Class A mission [NASA, 2008a]. Large satellites have one significant
advantage over SmallSats – they have operational characteristics that SmallSats cannot
physically do. For example, SmallSats cannot physically image the distant galaxies with high
resolution and for obvious reason cannot allow human space travel, like large satellites can. In
addition, larger satellites have traditionally had the advantage of generating public excitement
and appeal. Perhaps most important, large satellites fit within the traditional paradigm of how
satellites are developed and built.
Large satellites can host larger payloads and instruments onboard and can be supported by larger
subsystems such as solar arrays and more batteries. This can lead to better measurements and
sensing capabilities. Large satellites can also have the ability to host multiple sensors onboard
whereas SmallSats are typically limited to one or two payloads. For example the Mars Science
45

Laboratory (MSL) has over 10 main instruments on Curiosity rover and is a good example of
how many payloads can fit in on vehicle
*
[JPL, 2015].
Another advantage that large satellites have is their ability to have longer mission lifetimes. For
example, a larger satellite has the ability to incorporate large power systems that include extra
batteries and deployable solar arrays. Typically, large satellites will have parts redundancy such
as extra reaction wheels, batteries, solar panels, onboard computer, etc. This will allow the
system to have a backup resource just in case of a component failure.
3.4.2. Schedules, Reliability, and Risk
SmallSat missions provide much shorter schedules, comparable reliability, and significantly less risk
than traditional large satellite missions. SmallSat schedules are much shorter than for traditional
satellites. For instance, according to the Performance of Defense Acquisition System Annual Report
[DoD, 2013a], traditional major defense programs take on average 8.8 years in technology
development (Milestone B) and well over 10 years from research and material solution analysis
(Milestone A) to implementation. On average, SmallSats took less than half this time [Mosher,
1999]. Reliability of SmallSats (including single-string SmallSats) is essentially similar to that of
traditional large satellites according to a Goddard study [NASA, 2008b] of over 1,500 spacecraft
launched between 1995 and 2007.
Risk is defined as the probability of a negative event times the impact or consequences of that event.
Non-recurring cost for SmallSats is 1 to 2 orders of magnitude less than for traditional satellites
[NASA, 2008b]. Therefore, implementation risk is low due to low non-recurring cost and short
schedules. The consequences of failing to implement a SmallSat system will not endanger the larger,

*
Jet Propulsion Laboratory (JPL). 2015. Mars Science Laboratory Curiosity Rover, Instruments, NASA JPL, Website:
http://mars.jpl.nasa.gov/msl/mission/instruments/
46

more traditional system. Operational risk of SmallSats is also much lower than traditional systems
due to shorter operational life and the availability of spares (on orbit or on the ground) or back-up.
An immediate result of having shorter schedules, reduced risk, and increased reliability is that
SmallSats support the DoD objective of disaggregation [DoD, 2013b].
SmallSat missions are developed in less than 3.5 years [Mosher, 1999] while more traditional,
large satellites an average of 10 years to develop. SmallSats have comparable reliability to larger
satellite programs, despite often having single-string configuration and using COTS products.
SmallSats poses significantly less risk (both implementation and operational) than traditional
large satellite missions because failure rate is comparable to that of large satellites and
consequence of failure is reduced due to low development cost. In addition, a paper by Hurley
and Purdy at NRL “Designing and Managing for a Reliability of Zero” [2010], points out that
most of today’s space systems are designed for a reliability of zero, in the sense that for every
day that the system is not operational or the data available to the end user, it has a reliability of
zero. If the data isn’t there, it doesn’t matter to the warfighter who was killed or the scientist
who’s data was lost whether it wasn’t there because of a parts failure or because the program was
delayed or canceled due to more reviews or a lack of funding.
Development Schedules
There is a general consensus that a small project takes less time to complete than a large project. For
a space mission, “if you can’t build it in something like two years or less, it isn’t really a small
satellite.” [Fleeter, 2000] On the other hand, traditional major defense programs take 8.8 years in
development (Milestone B) and well over 10 years from Phase A to implementation [DoD, 2013a].
47

Table 3-8 shows the average length of time in years from contract award to launch and spacecraft
wet mass for 28 missions. Large satellites are defined as satellites with a dry mass greater than
2,500 kg, and a small satellite is defined as having a dry mass of less than 500 kg. As is shown
in the table, large satellites took approximately twice as long to develop as small satellites,
regardless of accounting for launch delays.
Table 3-8. Development Time Comparison for Small vs. Large Satellites [Mosher, 1999]
Parameter
Large
Satellites
Small
Satellites
Average Launch Mass (kg) 3,013 400
Median Launch Mass (kg) 2,787 295
Average Development Time (yr) 7.1 3.6
Median Development Time (yr) 6.0 3.5
Average Development Time without Launch Delays (yr) 6.6 3.25
Median Development Time without Launch Delays (yr) 6.0 3.0

The length of time from authorization to proceed (ATP) to launch is shown in Table 3-9 for large
and small satellites as defined above for 62 space missions launched between 1966 and 2013.
The data was originally developed by the Cost Analysis Division (CAD) at NASA HQ and is
now maintained with data collection support from the CAD, Independent Program Assessment
Office (IPAO), the Science Mission Directorate (SMD), Goddard Space Flight Center (GSFC),
Jet Propulsion Laboratory (JPL) and John Hopkins Applied Physics Laboratory (APL). The
database was supplemented with information on the SmallSats developed by Surrey Satellites
Technology Ltd. See Appendix 7.1 for the specific program names, dates, and dry masses.
Table 3-9. Time in years from ATP to Launch for Small and Large Satellites
Year Large (> 2500 kg) Small (< 500 kg)
1960 – 1970 N/A 3.3
1970 – 1980 5.9 3.6
1980 – 1990 10.8 4.6
1990 – 2000 10.0 3.1
2000 – Present 9.9 3.7
Total Average Duration 9.7 3.5
48

As Table 3-9 illustrates, the large satellites maintain a development schedule approximately
twice as long as a small satellite, regardless of when development occurred. Remarkably both
studies presented here illustrate that SmallSats take approximately half the time of large satellites
to develop. However, both studies have inherent deficiencies. A very small fraction of the
thousands of missions that exist were considered, 28 or 62 missions does not represent the whole
picture. The studies look at small satellites less than 500 kg in mass, but do not include nano- or
pico-satellites with dry launch masses of less than 10 kg and remarkably shorter development
schedules than larger satellites. If these two factors were addressed, it is expected that the
development duration for large satellites would be closer to 10 or more years, while the
development duration for small satellites would be closer to 2 years.
Reliability
Reliability of SmallSats is essentially similar to that of traditional large satellites, according to a
Goddard study of 1,505 spacecraft launched between 1995 and 2007 and summarized in Table
3-10. Table 3-10 indicates that failures for SmallSats is one similar to that for all spacecraft, and
that SmallSats do not fail at a significantly higher rate than large satellites. The difference in
percentage for Mission-Ending Spacecraft Failures is accounted for by studying spacecraft
configuration (single string or functionally redundant) and the cause of failure such as
environment, design, or parts/quality. Generally, SmallSats use a single-string configuration to
reduce mass, while large satellites use the higher-mass redundant configuration.
Table 3-10. Reliability of SmallSats Compared to All Spacecraft (Adapted from NASA [2008b])
Spacecraft
Type
Number of
Spacecraft
Launch
Failures
Mission-Ending
Spacecraft Failures
All 1,505 117 7.8% 53 3.5%
SmallSats 301 36 12.0% 17 5.6%
49

Does parts redundancy, essentially the duplication of critical units in case a backup is needed,
increase spacecraft reliability? In theory it should, as a failure in any unit of a single string will
cause the whole system to fail, while a dual string system provides a back-up in case the primary
fails. The cross-strapped design allows for any one unit that has failed to be bypassed. See
Figure 3-6 to see how the theoretical reliability values for different redundancy configurations
are related.

Figure 3-6. Theoretical Reliability of Single vs. Redundant Configurations [NASA, 2008b]
Contrary to the theory that redundancy increases reliability, the single string satellites had a
failure rate 4.6 times lower than the redundant satellites, as shown in Table 3-11, although the
mean time to failure was shorter for SmallSats. Although redundancy improves reliability, it also
lengthens the schedule for design, manufacturing and test, requires ground intervention to switch
to redundant units, does not correct for design flaws, and increases the complexity of the system.
50

Taking these effects of reality into account makes the reliability of redundant systems closer to
that of a single string system.
Table 3-11. SmallSat Failures by Configuration (Adapted from NASA [2008b])
Configuration Number of
SmallSats
Mission-Ending
Spacecraft Failures
Mean Time to
Failure (hr)
Single-string 154 10 6.5% 3,607
Redundant 23 7 30.4% 8,862
Unknown 124 0 - -

Selection of parts plays a large role in creating a reliable spacecraft; using defective or unreliable
parts will ensure an unreliable system. SmallSats take advantage of commercial off the shelf
(COTS) products in order to reduce the cost of parts and the time spent testing them. An
excellent example of this is NASA’s PhoneSat
*
program that uses smart phones and other COTS
products to build low cost nano-satellites. COTS products often provide technologies in smaller
packages, and the use of these products is what enables SmallSats to work. However, this
approach is contrary to the standards used by traditional large satellites, which created
technology readiness levels (TRL) and military standards (MILSTD) to ensure the reliability of
parts used on these programs. The discrepancy in approach begs the question, what impact does
the selection of parts have on spacecraft reliability?
Table 3-12. Failures by Cause (Adapted from NASA [2008b])
Cause of Failure Pre-1977 1977-1983 1983-1995
Design and Environment 42% 57% 36%
Parts and Quality 26% 20% 11%
Operational and Other 12% 10% 20%
Unknown 20% 13% 33%

Table 3-12 shows parts selection and quality is not the main contributing factor to spacecraft
reliability. In fact, design flaws or the space environment cause most failures, which suggests a

*
PhoneSat. 2013. Website: http://www.phonesat.org/index.php
51

shift from the debate of “COTS vs. stringent testing” to methods that will actually have an
impact on increasing spacecraft reliability.
Risk
Risk is defined as the probability of a negative event times the impact or consequences of that
event. In the case of SmallSats, the negative event is mission failure and the consequence is loss
of money spent developing and launching the SmallSat. Non-recurring cost for SmallSats are
typically orders of magnitude less than for traditional satellites. Therefore, implementation risk
is low due to low non-recurring cost and short schedule; failing to implement a SmallSat system
will not endanger the larger, more traditional system because it uses only a small fraction of
available resources. Operational Risk of SmallSats is also much lower than traditional systems
due to shorter operational life, and the availability of spares or back-up.
The objective of disaggregation by the Department of Defense is to divide the critical capabilities
of large and exquisite satellites into multiple platforms in order to create a more robust and
resilient system in the event of adversary attack. The findings from this report on the usage of
SmallSats for Earth Observing systems support several attributes of disaggregation. Specifically,
both drive down costs, use simpler systems, increase technology refresh opportunities, and
improve industrial base stability [DoD, 2013b].
3.4.3. Mission Cost
Small satellites typically cost much less than large satellites. There is a general consensus
regarding this and the cost model CERs in Sec. 3.2 support this as well. Generally speaking, it is
thought that small satellites and large satellites do not perform the same level of missions.
Therefore it is difficult to compare the two. However, as will be detailed in Sec. 4, it is possible
52

for small satellites to perform the same mission as larger satellites for Earth observation, even
with the same performance requirements, just at lower altitudes. Without doing this research to
show the effect orbit altitude has on mission cost, small satellites may continue to be under-
utilized as a means to reduce space mission cost. In addition, Baker and Worden [2008] contend
that “80% (or more) of program goals can be achieved for 20% of the cost by using small-
spacecraft solutions.” In Sec. 5.1, I hypothesize how this may be true for Earth observation
missions.
53

4 Background of Proposed Research
There are several benefits to flying at lower altitudes, as listed in Sec. 3.3.2. For example:
“If the resolution of the required system is already adequate, a reduction in orbit
altitude allows a smaller, cheaper, and lighter sensor to be used.”
This observation is particularly relevant, and is a key element in the basis for this research. For
passive optical sensors, typically diffraction limited, the aperture diameter for a fixed resolution
is directly proportional to distance. That is, decreasing the altitude by a factor of 2 will decrease
the required aperture diameter by a factor of 2 for a given resolution. For example, a 0.5-meter
resolution can be provided by a 0.88-meter diameter sensor flown at 800 km, or by a 0.44-meter
diameter sensor flown at 400 km, or by a 0.22-meter sensor flown at 200 km. By selecting a
smaller sized sensor, one is also decreasing the weight of the sensor and, therefore, the weight of
the entire system. For constant densities, mass is proportional to the cube of the linear
dimensions. Since spacecraft have slightly varying densities, space system mass is approximately
proportional to the cube of the linear dimensions. That is, reducing the altitude by a factor of 2,
reduces the mass by a factor of about 8. Equivalently, reducing the altitude by a factor of 4,
reduces the mass by a factor of approximately 64. This is a major advantage when trying to
reduce the mass and cost of a system. A general rule of thumb is to reduce the mass of the
system as much as possible while still continuing to meet mission requirements.
It is generally the case that the larger the system, the more costly it is to design, fabricate, and
place into orbit. Increasing the mass of a system generally correlates to an increase in total
mission cost. This relationship has been parametrically estimated to be true by cost models
developed by the professional cost modeling community. Existing cost models such as the
54

Unmanned Space Vehicle Cost Model (USCM) [Tecolote Research, 2002], Small Satellite Cost
Model (SSCM) [Aerospace Corp., 1996], and the NASA Instrument Cost Model (NICM)
[Habib-agahi, 2010] develop Cost Estimating Relationships (CERs) to predict cost using
preliminary mass estimates. These projections have historically correlated well with hardware
and system costs.
Changing the altitude directly affects the coverage capability. For example, the Area Access Rate
(AAR) is defined as the amount of new land the satellite sees over some amount of time [Wertz,
Everett, and Puschell, 2011]. If the minimum working elevation angle remains the same, the
AAR or coverage rate decreases with decreasing altitude. When coverage rate is a requirement,
additional satellites are required at lower altitudes to compensate for this decrease in coverage.
AAR is inversely proportional to altitude to the power of about 1.5 according to Eq. (4-11)
through Eq. (4-13). Therefore decreasing the altitude by a factor of 2 only increases the number
of additional satellites by a factor less than 2. How does this affect the cost for missions at the
lower altitudes? Building additional satellites only increases the total program costs by the
recurring cost, and typically with some learning curve applied. The non-recurring engineering is
only applied once; therefore, doubling the number of units, by no means doubles the total
program cost – it is actually much less.
I developed a method for this research that will model the total mission costs as a function of
performance through the use of systems engineering, physics, existing cost models, and common
cost estimating techniques. I call this process Performance-Based Cost Modeling (PBCM),
however, it is not meant to be another cost model. I intend to use PBCM to help quantify the
relationship between cost and performance. To this end, I define what I mean by cost, and what I
mean by performance. I define total mission cost as the total dollar amount spent on an entire
55

program. This includes non-recurring engineering (NRE), recurring, launch, production
(including learning curves), and amortization of costs. (Operations cost will not be included in
total mission costs (see Sec. 4.4.5). Performance, synonymous to measures of effectiveness
(MoE), is the first priority technical program objectives. The general process of PBCM is
repeated over a range of orbit altitudes, listed below and detailed in Secs. 4.1 – 4.5:
1. Specify the performance requirements and initial assumptions
2. Size the payload
3. Size the spacecraft bus to support the payload
4. Size the spacecraft wet mass
5. Determine number of satellites required
6. Estimates cost using existing cost model CERs
7. Determine launch costs
8. Determine non-recurring engineering and recurring engineering costs
9. Estimate total mission costs
10. Plot the relationship between total mission cost vs. altitude for fixed performance

4.1. Performance Requirements
For Earth observation missions, the performance requirements include resolution, coverage rate,
and mission lifetime. An example list of performance requirements is a satellite that can provide
imaging in the visible with a 0.5 meter resolution (at nadir), providing an area access rate
(coverage rate) of 14,200 km
2
/sec, over an 8 year period. Table 5-1 in Sec. 5.1 gives a sample list
of performance requirements as the baseline study. As will be discussed in Sec. 6.1, a range of
56

different possible performance requirements are be studied. The initial assumptions described in
Sec. 4.5 will allow us to perform the analysis.

4.2. Mass Estimations
To take advantage of existing cost models such as USCM, NICM, and SSCM, I will need a way
to estimate the payload mass, spacecraft bus mass, and spacecraft wet mass. These mass
estimates can directly be inserted into the Cost Estimating Relationships (CERs) to determine the
projected non-recurring engineering and recurring engineering costs (Sec. 4.4.3). The total
launch mass will also be needed to determine the total launch cost. There is more than one way
to estimate each of these mass values, but only the methods I used are described in this section.
Other methods can also work and will most likely produce slightly different values; however, it
is most important to remain consistent throughout the analysis. Each of these estimations will be
described below.
4.2.1. Instrument Sizing
The first step in estimating the mass of the spacecraft bus is to determine the size of the telescope
needed to meet the mission requirement. The aperture diameter, D of the telescope is directly
proportional to the orbital altitude, so I can easily estimate its linear dimensions by the following
relationship:
𝐷=
! !
!
(4-1)

57

where θ is the resolution requirement, h is the orbital altitude, and λ is the observation
wavelength. The payload mass will be determined following the estimate of the spacecraft bus
mass below.
4.2.2. Payload Mass, Power, and Data-rate
The estimation of the payload mass is straightforward. Space Mission Engineering: The New
SMAD [Wertz, Everett, and Puschell, 2011] provides average values for mass by subsystem as a
percentage of dry mass for LEO spacecraft. This is shown in Table 4-2. This method is
appropriate in order to allow us to compare missions with the same level of complexity. For LEO
satellites with propulsion capability, I can estimate the mass of the spacecraft bus by assuming
that the payload is 31% of the total dry mass of the spacecraft. Therefore, I can estimate the
spacecraft payload mass using Eq. (4-2). There is a reasonable alternative to estimating the mass
of the payload, namely, a bottoms approach where assumptions about the physical dimensions of
the instrument is made.
𝑀
!
=31% 𝑀
!
(4-2)
Payload power and data-rates, in addition to mass, are the three parameters used to estimate
payload costs using NICM. I found in Table 4-1, the average power (W) per kilogram of
spacecraft bus mass is 1.30 from 8 existing observation systems. On average, payloads consume
approximately 46% of total spacecraft power [Wertz, Everett, Puschell, 2011]. I use these two
values in Eq. (4-3) to determine the estimated instrument power requirement.
𝑃= 0.46% ∙ [1.30 ∙𝑀
!
] (4-3)
The data-rate at the 800 km altitude is assumed to be 800 Mbps, but this value does not matter
greatly when comparing different altitudes since the data rate will be scaled proportionately to
58

the Area Access Rate requirement (which is a function of altitude anyway). Data rate is
determined by the following relationship:
𝑅=
!!"
!!!
!"#
𝑅
!"#
(4-4)
where AAR is the area access rate calculated for each individual satellite at each altitude, AAR
req

is the coverage rate requirement, and R
ref
is the reference data rate that meets the AAR
requirement.
Table 4-1. Data from Existing Observation Systems
Satellite
Aperture
Diameter (m)
Satellite Dry
Mass (kg)
Spacecraft
Power (W)
Power/Bus
Mass (W/kg)
Dry Mass/
Aperture
3

GeoEye-1 1.1 1810 1360
GeoEye-2 1.1 2086
3850
1.85 1567
GlobalStar 400
1100
2.75
Kestrel Eye 0.23 14 1079
LeoStar-1 0.11 263
118
0.45
LeoStar-2 360
118
0.33
NanoEye 0.23 23
25.3
1.10 1773
OrbView-3 0.45 304
625
2.06 3336
Quickbird 0.6 995
1500
1.51 4606
SSTL-300-1 350
140
0.40
Average 1.30 2287

4.2.3. Spacecraft Bus
The spacecraft bus mass is estimated empirically by the relationship between aperture diameter
and satellite dry mass from data of existing observation systems. Table 4-1 shows the data used
to derive the average value of satellite dry mass from the cube of the payload aperture diameter.
This average value is used to estimate the spacecraft dry mass directly from the payload aperture
diameter found by Eq. (4-5).
59

𝑀
!
= 2287𝐷
!
(4-5)
For this research, to address design limitations, I will assume that the smallest system will be a
standard-sized 1U CubeSat with a spacecraft (bus and payload) weight of 1.33 kg [Munakata, et
al. 2009].
Table 4-2. Average Mass by Subsystem as a Percentage of Dry Mass for 4 Types of Spacecraft
[Wertz, Everett, Puschell, 2011]

4.2.4. Redundancy
Due to the long design life and relatively high cost of many traditional space systems, the
reliability of the spacecraft is very important. When parts or components fail on orbit, spacecraft
or their parts are very difficult and costly to replace; therefore, it is very common to design
spacecraft with parts and components redundancy. Some common examples of this are adding
more batteries or solar panels to ensure sufficient power at the end of the design life, extra
reaction wheels and control moment gyroscopes to support attitude control, a second computer to
60

ensure onboard processing, etc. This can dramatically increase the total mass (and cost) of the
spacecraft compared to a single-string system, commonly used on SmallSats.
In this model, I assume that the design life of the spacecraft is proportional to the orbit altitude.
That is, for example, a design life of 8 years at 800 km, 4 years at 400 km, 2 years at 200 km,
etc. At the much lower altitudes, the design life will be much shorter; and therefore should not
require as much redundancy in parts or components. I will assume a 5% decrease in mass for
every year the design life is decreased beginning with 8 years (e.g., 10% for 6 yrs, 20% for 4 yrs,
30% at 2 yrs, etc.). This will allow for a more realistic estimation for small satellites at lower
altitude that are designed to be single-string spacecraft.
4.2.5. Launch Mass
The launch mass, 𝑀
!
, or wet mass of the spacecraft is the sum of the spacecraft dry mass,
𝑀
!
(bus & payload) and propellant mass, 𝑀
!
. The total propellant mass required to maintain the
satellite through it’s’ design life is calculated iteratively using Eqs. (4-6) through (4-11).
𝑀
!
=𝑀
!
+𝑀
!
(4-6)
𝑀
!
=𝑀
!
1−exp
!∆!
!
!"
!
!
(4-7)
∆𝑉=
!"#$
!"
(4-8)
𝑣=
!!
!
−
!
!
(4-9)
𝛽=
!
!
!
!
(4-10)

61

𝑇= 2𝜋
!
!
!
(4-11)
where I
sp
is the specific impulse of the propellant, g
o
is the gravitational acceleration at the
Earth’s surface, β is the ballistic coefficient, C
D
is the satellite drag coefficient, A is the satellite
cross-sectional area along the velocity vector, M the mass of the satellite, ΔV is the total delta V
required to maintain the mission altitude for the design life of the satellite, ρ is the local
atmospheric density, r is the distance of the satellite from the central body (Earth), v is the orbital
velocity of the satellite, T is the orbital period of the satellite, 𝜇 is the gravitational parameter of
the central body, and a is the semi-major axis of the orbiting spacecraft. For circular orbits,
𝑎= 𝑟. For simplicity, I have chosen to use the average between the spacecraft wet mass and dry
mass for M when calculating the ballistic coefficient.
The mass of the satellite here is estimated to be the average of the satellite launch and dry
masses. The satellite cross-sectional area is calculated assuming a spherical spacecraft. The
satellite drag coefficient is assumed to be the same for every spacecraft in this model. The
atmospheric density is tabulated for each altitude in Wertz, Everett, and Puschell, [2011]. A
liquid monopropellant is assumed for each of the spacecraft where a single value of I
sp
is used.
Sellers, Paul, and Sweeting [1998] discuss cost-effective propulsion systems options for small
satellites, which may be appropriate for looking at other propulsion systems as well.

4.3. Estimating Number of Spacecraft
Independent of the mass estimates from Sec. 4.2, I can estimate the total expected number of
spacecraft needed as a function of altitude to satisfy the initial mission requirements. The number
62

of spacecraft needed will depend on three factors: (1) coverage rate, (2) mission lifetime, and (3)
system redundancy. These estimates will be important when I begin to compare small satellite
constellations with short design lives to large single satellite systems with longer design life.
4.3.1. Coverage Rate
Coverage rate in this model (for Earth coverage analysis) is commonly referred to as Area
Access Rate (AAR), which is defined as the rate at which the instrument is sensing or accessing
new land. The formula for AAR is given in Eq. (4-12), where ECA is the Earth Central Angle, T
is the orbital period, and K
A
= 2.55604187x10
8
for AAR measured in km
2
/sec [Wertz, 2009].
𝐴𝐴𝑅= 2𝐾
!
!"# (!"#)
!
(4-12)
𝐸𝐶𝐴=π−𝜀− asin
!
!
!"#!
!
(4-13)
The Earth Central Angle is calculated based on the assumption that all the spacecraft
(independent of orbital altitude) will be working at the same minimum working elevation angle,
𝜀. If all the spacecraft are designed to work at the same minimum working elevation angle, then
by Eq. (4-12), the AAR or coverage rate will be less at lower altitudes. For example, if the
coverage requirement is 14,200 km
2
/sec, the AAR at 200 km is 4,858 km
2
/sec; and therefore will
require approximately 3 more satellites on orbit at any given time. This is the method to
approximate the number of satellites required in orbit (size of the constellation) to meet the
coverage requirement.
4.3.2. Mission Lifetime
The number of spacecraft needed will also depend on the design life of each system. Take for
example, a mission at 200 km requires approximately 3 satellites in orbit but is designed to last
63

for 2 years. Thus, for an 8-year mission, approximately a dozen total spacecraft is needed to
complete the mission. That is, 3 new spacecraft will be built and launched after the first 2 years,
after the 4
th
year, and after the 6
th
year. Of course, any spacecraft can exceed its design life
expectation, but for the sake of modeling, I will ignore that probability. I can similarly estimate
the number of replacement spacecraft needed for missions at other altitudes. Note that a mission
flown at 800 km does not need any replacements if it is designed for the full 8-year mission.
Without doing a cost analysis, it is hard to tell which options are lower cost and by how much.
4.3.3. System Redundancy
The last step used in estimating the number of spacecraft needed to complete the mission is
planning for system redundancy, or estimating for launch failures. Launch failures have been
approximately 10% since the start of the space program and have not changed much since the
start of the space program [Wertz, Everett, Puschell, 2011]. If I assume that approximately 10%
of launches fail, I can easily estimate the number of additional spacecraft we’ll need to plan for.
For example, the total number of spacecraft needed for the 200 km mission increases from 12 to
13. Although this will not dramatically increase the number of extra spacecraft needed, it is
certainly worth including in this model. I will also note that system redundancy will be ignored
for missions that only require less than 3 spacecraft in total. Again, I can similarly estimate the
number of spacecraft needed at other altitudes. This completes the process for estimating the
total number of spacecraft for each mission altitude for the entire mission lifetime.

64

4.4. Cost Estimation
At this point, the individual spacecraft masses, and total number of spacecraft needed for the
entire duration of the mission as a function orbit altitude have been estimated. The steps taken to
estimate the total cost of the mission is discussed in this section. I start off by estimating the
upfront cost (NRE + RE), then calculate the total production cost for multiple units using a
learning curve, and then visit effects of amortization. Firstly, it is important to explain what is
meant by the total cost of the mission. I define total mission cost as the total dollar amount spent
on an entire program. This includes non-recurring engineering (NRE), recurring engineering
(RE), launch, production (including learning curves), and amortization of costs. Note that
operations cost is not included in this estimate (see Sec. 4.4.5).
4.4.1. Upfront Cost
Upfront cost in this model is defined as the cost associated with the Non-Recurring Engineering
(NRE) and Recurring Engineering (RE) costs of the Theoretical First Unit (TFU), including
launch. The NRE and RE costs are both defined below.
Non-Recurring Engineering – Non-Recurring engineering are costs associated with the labor
and material associated with designing, developing, fabricating, and testing a space vehicle
qualification test model plus program-peculiar ground support equipment (GSE). This is often
identified as the prototype approach and does not produce a flight unit. [Apgar, 2011]
Recurring Engineering – Recurring engineering costs are associated with the labor and material
of fabricating, manufacturing, integrating, assembling, and testing of follow-on space vehicle
flight hardware plus the effort associated with launch and orbital operations in support of the
programs [Apgar, 2011].
65

I model the expenditure over time as an upfront cost and a cost to be amortized over the life of
the program. The upfront cost is assumed to be paid upfront and is amortized over the life of the
program. There is also the declining cost/unit over time modeled by the learning curve described
below. It would be very awkward to try to incorporate all of this into an amortization model, so I
use a relatively simple model to take the effect into account.
The Unmanned Spacecraft Cost Model provides CERs for both NRE and RE costs. Both NICM
and SSCM do not. For purposes of this model, I need a way to estimate both the NRE and RE in
order to model the upfront and production costs. To determine the NRE and the RE costs from
both NICM and SSCM, I simply used the same percentages calculated from USCM separately
for missions at each orbit altitude. For example, if the NRE cost is 45% of the total program cost
calculated in USCM, this same value is also used to estimate the NRE cost in both NICM and
SSCM as well; and the RE cost is done the same way. Launch costs is a recurring engineering
cost and is estimated separately, and is discussed in Sec. 4.4.4.
4.4.2. Cost Models
The Unmanned Space Vehicle Cost Model, Version 8 (USCM) was developed by Tecolote
Research for the US Air Force, Space and Missile Systems Center [Tecolote Research, 2002].
The CERs were derived using statistical regression techniques from 44 satellites to support
parametric cost estimates of unmanned, Earth-orbiting space vehicles. USCM provides cost
estimates for both NRE and RE. Because USCM does not have CERs to predict cost for space
vehicles with observation payloads, I will be adding CERs from NICM in order to estimate the
cost for Earth observation systems.
66

The NASA Instrument Cost Model (Version IIIC), was developed by the Jet Propulsion
Laboratory in 2010 from 159 data points obtained from instrument contractors [Habib-agahi,
2010]. This model predicts the cost of development plus one flight unit, also known as the
theoretical first unit (TFU). I use NICM to predict the cost of the payload. This cost combined
with cost estimates from USCM, I am able to estimate the total mission cost.
The third cost model we’re using is the Small Satellite Cost Model (SSCM) and is used to
compare with estimates produced by USCM and NICM since I will be comparing large
traditional satellites and small spacecraft. The Aerospace Corporation [1996] developed this
parametric cost model for predicting development and TFU cost for smaller Earth-orbiting and
near-planetary spacecraft. The CERs are based on 53 individual satellites. One of the main
reasons for the development of the SSCM is due to the fact that USCM was systematically
overestimating the cost of small satellites
*
.
4.4.3. Cost Estimating Relationships (CERs)
There are 3 cost models that are used to determine the costs, each with their own Cost Estimating
Relationships (CERs). CERs are statistically-based cost-predicting algorithms derived from
normalized historical databases [Apgar, 2011]. They are basically equations that estimate cost,
by taking inputs such as mass, power, and data-rate to predict them. The three models used for
this study are the Unmanned Space Vehicle Cost Model (USCM), NASA Instrument Cost Model
(NICM), and the Small Satellite Cost Model (SSCM) and can all be found in Space Mission
Engineering: The New SMAD [Wertz, Everett, and Puschell, 2011]. In summary, USCM is
primarily used for estimating cost for more traditional (large) spacecraft, SSCM as the title

*
Aerospace Corporation. 2015. SSCM Overview. Website: http://www.aerospace.org/expertise/technical-resources/small-
satellite-cost-model/sscm-overview/
67

suggests is more suitable for small spacecraft, and NICM predicts cost for sensing instruments.
Each of the following cost models are consistently being updated. However, the CERs that I will
be using are published in Space Mission Engineering: The New SMAD.
USCM
The Unmanned Space Vehicle Cost Model conveniently provides both NRE and RE costs, and
are given in FY$K2010 in Eqs. (4-14) through (4-23). However it does not provide a CER for
Earth observation payloads. Equations (4-24) and (4-25) provide the fraction of NRE over
upfront cost, and the fraction of RE over upfront cost, respectively.
𝑁𝑅𝐸
!"#$%$&#'( !"#
= 108.0 𝑀
!
(4-14)
𝑁𝑅𝐸
!"&!
= 0.195(𝑁𝑅𝐸
!"#$%$&#'( !"#
+𝑁𝑅𝐸
!"#$%"&
) (4-15)
𝑁𝑅𝐸
!"#$"%& !"#"!
= 0.357 (𝑁𝑅𝐸
!"#$%$&#'( !"#
+𝑅𝐸
!"#$%"&
+𝑁𝑅𝐸
!"&!
) (4-16)
𝑁𝑅𝐸
!"#
= 0.432 ∙2.244 𝑁𝑅𝐸
!"#$%$&#'( !"#
!.!"#
(4-17)
𝑁𝑅𝐸
!"#$
=𝑁𝑅𝐸
!"#$%$&#'( !"#
+𝑁𝑅𝐸
!"&!
+𝑁𝑅𝐸
!"#$"%& !"#"$
+𝑁𝑅𝐸
!"#
(4-18)
𝑅𝐸
!"#$%$&#'( !"#
= 283.5𝑀
!
!.!"#
(4-19)
𝑅𝐸
!"&!
= 0.124(𝑅𝐸
!"#$%$&#'( !"#
+𝑅𝐸
!"#$%"&
) (4-20)
𝑅𝐸
!"#$"%& !"#"$
= 0.320 (𝑅𝐸
!"#$%$&#'( !"#
+𝑅𝐸
!"#$%"&
+𝑅𝐸
!"&!
) (4-21)
𝑅𝐸
!""#
= 5719.4 (4-22)

68

𝑅𝐸
!"#$
=𝑅𝐸
!"#$%$&#'( !"#
+𝑅𝐸
!"&!
+𝑅𝐸
!"#$"%& !"#"$
+𝑅𝐸
!""#
(4-23)
𝐹
!"#
=
!"!
!"#$
!"!
!"#$
!!!
!"#$
(4-24)
𝐹
!"
=
!!
!"#$
!"!
!"#$
!!!
!"#$
(4-25)
where subscripts IA&T, AGE, LOOS stand for Integration, Assembly, and Test, Aerospace
Ground Equipment, and Launch Operations and Orbital Support, respectively.
NICM
The NASA Instrument Cost Model provides a CER for Earth observation payloads. This CER
estimates NRE plus the cost of the theoretical first unit. I will call this Development Plus First
Unit Cost (DFU), and is given in FY$K2010:
𝐷𝐹𝑈
!"#$%"&
= 1163 𝑀
!
!.!"#
𝑃
!.!"#
(𝑅)
!.!"#
(4-26)
𝐷𝐹𝑈
!"#"$!"!#$
= 0.07124 𝐷𝐹𝑈
!"#$%"&
!.!"#$
(4-27)
𝐷𝐹𝑈
!"
= 0.4931 𝐷𝐹𝑈
!"#$%"&
!.!"#$
(4-28)
𝐷𝐹𝑈
!"
= 0.1427 𝐷𝐹𝑈
!"#$%"&
!.!"##
(4-29)
𝐷𝐹𝑈
!"&!
= 0.1457 𝐷𝐹𝑈
!"#$%"&
!.!!"#
(4-30)
𝐷𝐹𝑈
!"#$
=𝐷𝐹𝑈
!"#$%"&
+ 𝐷𝐹𝑈
!"#"$%&%#'
+ 𝐷𝐹𝑈
!"
+ 𝐷𝐹𝑈
!"
+𝐷𝐹𝑈
!"&!
(4-31)
where M
L
is the payload mass, P is the instrument power requirement, and R is the data-rate.
Subscripts SE and PA stand for Systems Engineering and Product Assurance, respectively. As
69

mentioned in Sec. 4.4.1, to determine the NRE and the RE costs for payloads using NICM, I
simply use the same percentages calculated from USCM. For example, if the NRE cost is 45% of
the total program cost calculated in USCM, this same value is also used to estimate the NRE cost
in both NICM as well; and the RE cost is done the same way. The same is done in finding the
NRE and the RE cost for all CERs using SSCM. I use Eqs. (4-32) and (4-33) to find the NRE
and RE of the payload.
𝑁𝑅𝐸
!"#$
=𝐹
!"#
𝐷𝐹𝑈
!"#$
(4-32)
𝑅𝐸
!"#$
=𝐹
!"
𝐷𝐹𝑈
!"#$
(4-33)
Now that I have the individual NRE and RE from both USCM and NICM, I can find the total
NRE and RE costs:
𝑁𝑅𝐸
!"#$%
=𝑁𝑅𝐸
!"#$
+𝑁𝑅𝐸
!"#$
(4-34)
𝑅𝐸
!"#$%
=𝑅𝐸
!"#$
+𝑅𝐸
!"#$
(4-35)
SSCM
The Small Satellite Cost Model only provides CERs for the Development plus First Unit Cost
(DFU) as well, and is given in FY$K2010:
𝐷𝐹𝑈
!"#$%$&#'( !"#
= 1064+35.5 𝑀
!
!.!"#
(4-36)
𝐷𝐹𝑈
!"#$%"&
= 0.400 𝐷𝐹𝑈
!"#$%$&#'( !"#
(4-37)
𝐷𝐹𝑈
!"&!
= 0.139 𝐷𝐹𝑈
!"#$%$&#'( !"#
(4-38)

70

𝐷𝐹𝑈
!"#$"%& !"#"$
= 0.229 𝐷𝐹𝑈
!"#$%$&#'( !"#
(4-39)
𝐷𝐹𝑈
!""#
= 0.061 𝐷𝐹𝑈
!"#$%$&#'( !"#
(4-40)
𝐷𝐹𝑈
!"#
= 0.066 𝐷𝐹𝑈
!"#$%$&#'( !"#
(4-41)
𝐷𝐹𝑈
!!"#
=𝐷𝐹𝑈
!"#$%$&#'( !"#
+𝐷𝐹𝑈
!"#$%"&
+𝐷𝐹𝑈
!"&!
+𝐷𝐹𝑈
!""#
+𝐷𝐹𝑈
!"#
(4-42)
As mentioned in Sec. 4.4.1, to determine the NRE and the RE costs for each cost element using
SSCM, I simply use the same percentages calculated from USCM. For example, if the NRE cost
is 45% of the total program cost calculated in USCM, this same value is also used to estimate the
NRE cost in SSCM as well; and the RE cost is done the same way:
𝑁𝑅𝐸
!!"!
!
=𝐹
!"!
!
𝐷𝐹𝑈
!!"!
!
(4-43)
𝑅𝐸
!!"!
!
=𝐹
!!
!
𝐷𝐹𝑈
!!"!
!
(4-44)
where i represents the various cost elements (i.e., spacecraft bus, payload, IA&T, program level,
launch operations and orbital support (LOOS), and aerospace ground equipment (AGE)).
4.4.4. Launch Cost
For fixed resolution, coverage, and lifetime requirements, there is a small number of larger
satellites at high altitudes and a larger number of much smaller satellites at low altitudes.
Generally, the cost per kilogram will be higher for the smaller satellites, and the total mass
launched to orbit (the number of satellites times the mass per satellite; see Table 5-2, line 17) can
vary with altitude and design life. Space Mission Engineering: The New SMAD provides cost
data on existing launch vehicles shown in Table 4-3. I then derive an empirical formula for
71

estimating launch cost (per kilogram) by fitting these data-points on a linear cost curve
represented in Eq. (4-45), where 𝐶
!"#$%!
is in FY13$K/kg.
𝐶
!"#$%!
= 26.489−0.0015𝑀
!
(4-45)
Table 4-3. Cost Data for Existing Launch Vehicles [Wertz, Everett, and Puschell, 2011]

Total remaining launch cost is then given in Eq. (4-46) below. Of course, there are other ways to
estimate launch cost for a given spacecraft. However, this particular estimation allows a fair way
to estimate launch cost based on the launch mass (or wet mass) of the system independent on
current launch systems. This is not to get confused with the estimation of a particular launch
vehicle design. Koelle [1998] provides a good reference on the basic definition and application
on “cost engineering” which means to design a vehicle system for minimum development cost
and for minimum operations cost.
𝐶
!
=𝑁 ∙𝐶
!"#$%!
∙𝑀
!
(4-46)
4.4.5. Operations Cost
Mission cost in this study does not include operations costs. So far as I are aware, none of the
publicly available space cost models that include operations cost break that cost down into
elements that reflect the size, cost, or complexity of the spacecraft that is being operated.
Vehicle
Capacity to
LEO (kg)
Launch to
LEO Cost/kg
(FY13$K)
Pegasus XL 443 $43.64
Taurus 1,380 $19.77
Minotaur IV 1,650 $13.99
Athena 2,065 $16.61
Atlas 2AS 8,618 $16.19
Ariane 44L 10,200 $15.77
Falcon 9 10,450 $5.68
72

Nonetheless, it is reasonable to assume that we won't use the same Operations Concept for a
$200 million spacecraft with a 10-year intended life as we would with a $2 million spacecraft
with a 2-year intended life. There is also empirical evidence that this difference is real, as
discussed below. Creating an operations cost model that is a function of the spacecraft size or
complexity will likely be a challenging task. Adding operations cost to the current model will
likely either move the model vertically, without changing the shape of the curve or possible tilt it
a bit further in the in the direction of favoring small satellites. I do not expect the change to be
substantial in either case and therefore do not expect the exclusion of operations cost to hurt the
findings in this study. I would welcome any data that others may have that reflects the impact of
spacecraft size and complexity on operations cost.
Typically, operations cost depends on the following factors [Apgar, 2014]:
1. The number, complexity, and location of control and other ground stations and whether
the control stations are dedicated to a single program (e.g., GPS) or allocated to multiple
programs (e.g., JPL robotic missions)
2. The number of operators and hours per day required, the requirement for data recovery or
additional data processing, and the level of automation (See Chap. 28 of Wertz, Everett,
and Puschell [2011])
3. The amount of on-going R&D required (e.g., the need to upgrade operating software)
4. The amount of contactor support during the early years of the mission
Naturally, small satellites have lower operating cost. NEAR, Clementine, SAMPEX, ALEXIS,
UoSat-05 are all examples of low cost small satellite programs with low operations costs.
Operations costs for these specific missions were approximately 5-10% of their total mission
73

cost, and their associated data can be found in Wertz and Larson [1996]. Therefore, multiple
SmallSats flying at lower altitudes can have comparable operations cost to a single traditional
satellite mission.
Chapter 6 of Wertz and Larson [1996] gives detailed methods and concepts for reducing the cost
of mission operations. Lee and Santo [1998] describe how the use of spacecraft autonomy can
reduce mission operations costs. The simplest way to reduce operations cost is to reduce
operations. Lewin [1998] also describes how automation can achieve low operations cost even
for a constellation of satellites (i.e., ORBCOMM).
4.4.6. Production Cost with Learning Curve
When building multiple units of spacecraft, learning curves can be used. Learning curves is the
concept that expresses the fact that building two of something cost more than building a single
unit, but not twice as much. The Wright learning curve [NASA, 2008a] used in this model is the
percentage reduction in cumulative average cost when the number produced is doubled. The cost
with producing the first unit is called the Theoretical First Unit (TFU). The total production cost,
C
P
, for N remaining units with a fixed learning curve, S, can be expressed by:
𝐶
!
=𝑇𝐹𝑈 ⋅𝑁
!!!"#
!
!
!
(4-47)
An 85% learning curve is a general rule of thumb used in the aerospace industry according to the
NASA Cost Estimating Handbook [2008a], but to be slightly more conservative, I will assume a
90% learning curve for the baseline mission.
74

4.4.7. Amortization Cost
Like most things, money isn’t free. Even ignoring inflation, spending $1M today will cost more
than spending $1M next year, and much more than spending $1M ten years from now. If we
don’t have the money, we will have to borrow it and pay interest, and if we do have the money,
we will have to forego using it for some alternative purpose capable of generating interest or
income. Mathematically, these two approaches are the same and represent the time value of
money. The future value of money is what a given amount of money would be worth at some
time in the future and depends mathematically on how often interest is paid, but the differences
are not great and can be ignored for most system design purposes. Therefore, putting off
spending reduces the real cost.
Amortization is the process of paying off over time. Normally, we amortize a loan by making a
fixed number of equal payments. Each payment covers the interest due, with the remainder
applied to the principal amount. The total amortization cost, C
A
, for 𝑛 equal payments at an
interest rate, 𝑖 (per payment period) is given by:
𝐶
!
=𝑛𝑉
!
!! !!!
!!
(4-48)
where V represents the principal value. According to the relationship expressed in Eq. (4-48), it
will be more costly to spend all the money upfront and amortizing the cost over a long period of
time, rather than paying a fraction of the cost upfront, and dividing the rest over that time period
in some fashion. For the baseline mission, I will assume an interest rate of 8%, which on
average, will provide a cumulative cost savings effect of amortization, α, of approximately 19%
for 8 year missions, or 8% for 4 year missions, and will be applied to all postponed costs.
Missions with spacecraft that are designed with shorter design lives will benefit the greatest from
75

this because they have the most postponed costs. Alternatively, for the 800 km mission, the
spacecraft designed to last 8 years will need to build all at once, and will have no postponed
costs associated with it and, in comparison, will cost more.
4.4.8. Total Mission Cost
The total mission cost, 𝐶
!
, as mentioned previously, is the total dollar amount spent on an entire
program. This includes non-recurring engineering (NRE), recurring engineering (RE), launch,
production (including learning curves), and amortization of costs. Operations cost is not included
in this estimate. The cost of the theoretical first unit cost, TFU, plus the total remaining
production cost and launch cost with savings due to amortization applied gives the formula for
total mission cost in Eq. (4-48):
𝐶
!
=𝑇𝐹𝑈+ (𝐶
!
+𝐶
!
)(1−𝛼) (4-49)
4.4.9. Constant-Year-Dollars
Inflation represents the decreasing value of money over time. Inflation rates estimated by the
government are shown in Table 4-4, adapted from Apgar [2011].
For consistency in referring to cost estimates, and to minimize confusion in the review of cost
estimates, cost should be stated in constant-year-dollars [Apgar, 2011]. Table 4-4 will allow you
to convert cost from one fiscal year to another fiscal year. For example, to convert a cost C,
from FY2010 dollars to FY2013 dollars using the table, you would use the following formula:
𝐶
!"!"#$
=
!.!"!#
!.!"#$
𝐶
!"!"#"
(4-50)
Using this technique, we can typically ignore inflation altogether. In this study, I have converted
all cost to FY2013.
76

Table 4-4. Inflation Factors Relative to the Year 2012
Fiscal
Year
Inflation Factor to
Base Year 2012
Fiscal
Year
Inflation Factor to
Base Year 2012
2005 0.8583 2016 1.0857
2006 0.8867 2017 1.1089
2007 0.9108 2018 1.1326
2008 0.9375 2019 1.1568
2009 0.9501 2020 1.1815
2010 0.9701 2021 1.2067
2011 0.9850 2022 1.2324
2012 1.0000 2023 1.2588
2013 1.0201 2024 1.2856
2014 1.0413 2025 1.3131
2015 1.0630 2026 1.3411

4.5. Summary of Assumptions
All of the input assumptions are summarized in Table 4-5. Because there are a large number of
assumptions, I looked at the impact of how varying each of the input assumptions affects the
final results. Varying the inputs assumptions changed the numerical values of the results,
essentially moving the result curves up and down (in Figure 5-1), but does not change the
relative results or the nature of the conclusions.

77

Table 4-5. Input Data for the Baseline Mission.

This model allows the user to change any of the assumptions very easily. A more detailed assessment
will be done to determine the relationships and their impact on changing each assumption in
Appendix 7.2 The summary of other assumptions we’ve made to generate the results are
consolidated in the listed below:
• The optical payload assumes diffraction limited optics
• Space system mass is proportional to the cube of the linear dimensions – equivalent to
saying that similar observation systems have about the same density [Reeves, 1999]
• Non-redundancy mass reduction factor – A 5% reduction in estimated mass for every year
the design life is reduced starting at 8 years (e.g., 10% mass reduction for 6 yrs, 20% for 4
yrs, 30% at 2 yrs)
• All systems are designed using a liquid monopropellant
Assumptions Value
Resolution (m) 0.5
Area Access Rate (AAR) at 800 km Altitude (km
2
/s) 14,217
Mission Life Requirement (yrs) 8
Wavelength to Observe (nm) 550
Spacecraft/Payload Average Density (kg/m
3
) 79
Propellant Density (kg/m
3
) 1000
Dry Mass/ Aperture
3
2287
Payload % of Total S/C Dry Mass 31%
Spacecraft Power/Spacecraft Dry Mass (W/kg) 1.3
Payload Power Percentage of Spacecraft Power (W) 46%
Spacecraft Datarate at 800 km Altitude (kbps) 800,000
Drag Coefficient 2
Solar State (Min, Mean, Max) Mean
Minimum Working Elevation Angle (deg) 30
Percentage of Launches that Fail 10%
Min. No. Sats for No System Redundancy 2
Spacecraft Propellant Isp 235
Learning Curve 90%
Amortization Rate 8%
Cumulative Savings Effect of Amortization 19%
78

• All missions are flown in a circular orbit
• All missions work at the same minimum elevation angle of 30 deg
• Design life is proportional to altitude (e.g., 8 yrs at 800 km, 2 yrs at 200 km)
• Wright learning curve for multiple units
• Costs postponed due to spacecraft being built and launched later are reduced to Present
Value to account for the value of delayed spending

79

5 Research Hypothesis & Preliminary Results
5.1. Research Hypothesis
It is hypothesized that by flying multiple small satellites at lower altitudes than traditional
systems, Earth observation satellites have the potential for much lower overall mission costs.
Surrey Satellite Technology Limited (SSTL), Skybox, and Planet Labs are all examples of
companies that have successfully flown or are currently designing systems that are much lower
cost and comparable in performance to larger systems flown at higher altitudes. As is the case
with laptops and cell phones today, there is very little incentive to designing systems to last over
5 – 7 years because they can be expected to be superseded by the next generation. The key
implication of the hypothesis is that it will be possible to build and operate spacecraft at lower
altitudes, accept a shorter lifetime, and gain the benefits of a different operating concept. If it is
possible to provide required performance levels by using smaller, (shorter-lived), satellites at
lower altitudes, the possibility of using smaller, launch-on-demand boosters also becomes more
feasible [Eves, 2013]. There may always be a debate on whether lower-cost small satellites can
be comparable to traditional systems in performance and still meet mission goals successfully.
Space experts and scientist often belittle the contribution of small, low-cost space missions
[Baker and Worden, 2008]. A major challenge with this research is that the results and findings
may not be in the best interest of a subset of the aerospace community, in that this research is
intended to find quantitative results and validate that flying at lower altitudes may have a major
potential for cost reduction in space missions.
By reducing the altitude, one can reduce the size of the spacecraft, and thereby have a significant
impact on cost. This is shown in Figure 5-1. The preliminary results clearly show that using
smaller satellites at lower altitudes can provide much lower cost missions for an observation
80

system while achieving the same performance requirements in terms of both resolution and
coverage. The most substantive conclusion is that by significantly reducing the altitude of an Earth
observation system, we can achieve the same performance in terms of resolution and coverage, but
at dramatically lower cost. Why is that the case? Basically, if we reduce the altitude by a factor of
2, we will also reduce the sensor aperture and linear dimensions of the spacecraft by a factor of 2.
This reduces the volume and mass of the spacecraft by a factor of 8, which, according to the
traditional mass-based cost models, reduces the cost by a factor up to 4.5. There will likely be the
need for more spacecraft at the lower altitude because of reduced coverage per satellite and
possibly a shorter design life, or greater atmospheric drag, but even with more spacecraft, it will be
a much lower cost and more robust system that is less susceptible to spacecraft or launch failures.
This path has the potential to be an important option for Earth observing systems, particularly in
times of ongoing belt tightening and budget cut. It is also predicted that there will be a concavity in
the mission cost. vs. orbit altitude curves, i.e., an optimal cost point will exist for each set of
performance requirements. This is a testable hypothesis. The approach is described in Sec. 6. In
summary, my three research hypotheses are as follows:
1. There will be a concavity in the total mission cost vs. orbit altitude curve.
2. The optimal altitude for Earth observation missions will be at or below 500 km regardless
of performance requirements.
3. Flying multiple small satellites at lower altitudes than traditional systems, Earth
observation satellites have the potential for much lower overall mission costs.

81

5.2. Preliminary Results
To help visualize the research hypothesis, baseline performance requirements, reflective of a
typical LEO Earth observation mission were chosen and are displayed in Table 5-1. Using the 10
steps discussed in Sec. 4, the total mission cost to fly systems over a range of altitudes in LEO
was determined and will be described in this section. It shall be noted that any conclusions
resulting from this baseline mission will be specific to this type of mission with the given
performance requirements.
Table 5-1. Baseline mission requirements
Requirement Baseline Mission
Sensing Requirement Visible EO Imaging
Resolution at nadir 0.5 meter
Coverage 14,200 km
2
/sec
Mission lifetime 8 years

Three mission altitudes of 200 km, 400 km, and 800 km were selected for comparison, and the
technique and assumptions were applied for each altitude as described in Sec. 4. Real observation
system examples were provided for reference, which include Quickbird
*†
, and GeoEye-2
‡
[GeoEye,
2013; Space News, 2012]. The cost associated with Quickbird and GeoEye-2 was found publicly.
These costs are taken to be a minimum. I start off by determining the payload aperture diameters
using diffraction-limited optics and we see that the aperture is linearly proportional to the mission
altitude (i.e., 0.22 m at 200 km, 0.44 m at 400 km, and 0.88 m at 800 km). As can be seen in Table
5-2 the payload power and data-rate scale proportionally to the mission altitude as well. For a fixed
resolution, the spacecraft mass required at 200 km is 17 kg, but is almost two orders of magnitude
larger (1,559 kg) at 800 km. This is a very significant difference in mass and will generate a
substantial difference in mission cost, as will be seen in Table 5-3 and Table 5-4.

*
Digital Globe. 2013. Quickbird Datasheet. DS-QB 07/13.
†
Spaceflight Now. 2000. “Commercial Eye-in-the-sky Appears Lost in Launch Failure.” November 21.
‡
Space News. 2012. “NGA Letters Cast Cloud Over GeoEye’s Enhanced View Funding.” June 23.
82

The area access rate (AAR) is less at lower altitudes, and therefore will require additional
satellites to satisfy the coverage rate requirement of 14,217 km
2
/sec. To support the same
coverage rate a single satellite at 800 km, requires 2.9 satellites at 200 km and 1.6 satellites at
400 km. Then, based on the design life of each spacecraft and accounting for launch failures, one
can determine the number of satellites required for the entire 8-year mission. Notice that, I
allowed for the use of fractions of satellites for design simplicity and to supply a smoother
display of results. For the baseline mission providing 0.5 m resolution in the visible at 14,217
km
2
/sec, for 8 years, our 3 options are:
1. 1 traditional large satellite (1,559 kg) flown at 800 km
2. 3.6 moderate-size satellites (156 kg each) flown at 400 km
3. 12.9 SmallSats (17 kg each) flown at 200 km
The projected cost values, in constant year dollars, for several cost items using USCM and
NICM are displayed in, and for comparison using SSCM in Table 5-4. The key cost values here
are:
• The total upfront cost (line 2)
• The remaining recurring cost with learning curve (line 6)
• The total adjusted system cost after amortization (line 12)

83

Table 5-2. Physical Parameters of 3 Select Mission Altitudes

Table 5-3. Cost Predictions for the 3 Selected Altitudes using USCM8 and NICM

Table 5-4. Cost Predictions for the 3 Selected Altitudes using SSCM

Quickbird GeoEye-2
1 Orbital Altitude (km) 200 400 800 482 681
2 Resolution (m) 0.5 0.5 0.5 0.65 0.32
3 Payload Aperture Diameter (m) 0.22 0.44 0.88 0.60 1.10
4 Spacecraft Dry Mass (kg) 24.4 194.8 1,558.6 995.0 2,086.0
5 Non-Redundancy Mass Reduction 30.0% 20.0% 0.0%
6 Corrected Spacecraft Dry Mass (kg) 17.0 155.9 1,558.6
7 Spacecraft Wet Mass (kg) 292.5 181.2 1,559.4 1,028 2,540
8 Payload Power (W) 10.2 93.2 932.0
9 Payload Datarate (kbps) 273,345 489,309 800,000
10 Spacecraft Area Access Rate (km
2
/sec) 4,858 8,696 14,217 10,034 12,819
11 Satellite Orbital Period (min) 88.5 92.6 100.9 94.2 98.4
12 Spacecraft Design Lifetime (yrs) 2 4 8 4.82 6.81
13 No. of Sats Needed for Same Coverage at Any Given Time 2.9 1.6 1.0 1.4 1.1
14 Number of Satellites Required for Entire Mission 11.7 3.3 1.0 2.4 1.3
15 Number of Redundant Satellites 1.2 0.3 0.0 0.2 0.0
16 No. of Satellites to Build w/ System Redundancy* 12.9 3.6 1.0 2.6 1.3
17 Total Launch Mass (kg) 3,767 652 1,559 2,659 3,309
* Note that fractions of satellites have been allowed in this model for purposes of comparison simplicity and a smoother display of results
Examples Model Predictions Physical Parameters
Quickbird GeoEye-2
1 Orbital Altitude (km) 200 400 800 482 681
2 Total Upfront Cost (FY13$M) $47.45 $178.85 $991.29 $87.5 $835.0
3 Total NRE Cost (FY13$M) $14.89 $100.79 $708.75
4 TFU or T1 Cost (FY13$M) $24.95 $73.31 $244.88 $60.0 $784.4
5 Total RE Production Cost w/ Learning Curve (FY13$M) $217.84 $217.07 $244.88 $134.3 $981.7
6 Remaining RE Production Cost w/ Learning Curve (FY13$M) $192.90 $143.76 $0.00 $74.3 $197.3
7 Average RE Unit Cost per Spacecraft (FY13$M) $16.92 $60.35 $244.88 $51.9 $784.4
8 Nth (Last) Unit Cost (FY13$M) $14.62 $55.65 N/A $50.6 N/A
9 Equivalent Present Value of Amortized Cost (FY13$M) $203.94 $123.97 $0.00 $87.8 $170.1
10 Total System Cost Before Amortizing (FY13$M) $299.27 $331.93 $991.29 $161.8 $1,032.3
11 Total System Cost to be Amortized (FY13$M) $251.82 $153.07 $0.00 $74.3 $197.3
12 Total Adjusted System Cost After Amorizing (FY13$M) $251.39 $302.82 $991.29 $175.3 $1,005.1
Examples Model Predictions Cost Estimates - USCM8 and NICM (from SME)
Quickbird GeoEye-2
1 Orbital Altitude (km) 200 400 800 482 681
2 Total Upfront Cost (FY13$M) $12.27 $48.05 $790.88 $87.5 $835.0
3 NRE Cost (FY13$M) $2.30 $26.59 $569.37
4 TFU or T1 (FY13$M) $2.35 $16.71 $183.84 $60.0 $784.4
5 Total RE Production Cost w/ Learning Curve (FY13$M) $20.49 $49.48 $183.84 $134.3 $981.7
6 Remaining RE Production Cost w/ Learning Curve (FY13$M) $18.14 $32.77 $0.00 $74.3 $197.3
7 Average RE Unit Cost per Spacecraft (FY13$M) $1.59 $13.76 $183.84 $51.9 $784.4
8 Nth (Last) Unit Cost (FY13$M) $1.37 $12.68 N/A $50.6 N/A
9 Equivalent Present Value of Amortized Cost (FY13$M) $62.41 $34.08 $0.00 $87.8 $170.1
10 Total System Cost Before Amortizing (FY13$M) $89.33 $90.13 $790.88 $161.8 $1,032.3
11 Total System Cost to be Amortized (FY13$M) $77.07 $42.08 $0.00 $74.3 $197.3
12 Total Adjusted System Cost After Amorizing (FY13$M) $74.68 $82.13 $790.88 $175.3 $1,005.1
Examples Model Predictions Cost Estimates - SSCM (1996) (from SME)
84

The total upfront cost for both the 200 and 400 km mission are much less than the upfront cost
for the 800 km mission. However, both missions at the lower altitude have additional costs
associated with the mission (i.e. the remaining production cost). Even without adjusting the cost
due to advantages of amortization, the total system cost (Table 5-3 and Table 5-4, line 10) shows
that at lower altitudes the life-cycle costs are much less, even with many more satellites to build.
(Again, the life-cycle cost does not include operations cost. Section 4.4 described how adding
operations cost would not impact the relative results of the study.) Results from Table 5-3 and
Table 5-4 notably different values because USCM is developed by parametric cost modeling of
traditional large satellite systems, while the SSCM is derived from parametric cost modeling of
SmallSats [Apgar, 2011].
The model estimates the required mass to operate at each altitude for a given resolution and
coverage rate, and then inserts them into separate costs models (USCM & NICM, and SSCM). I
ran the model twice over a range of altitudes: first with projections from USCM and NICM, and
again with projections from SSCM, and then plotted them on the same graph for comparison.
This means the missions are compared within the same class each time. In a sense, what I am
doing is comparing apples to apples and oranges to oranges at the same time.

85

Figure 5-1. Total Mission Cost vs. Orbit Altitude for Fixed Performance Requirements

The preliminary result corresponding to the baseline mission is shown in Figure 5-1. This figure
displays the total mission cost vs. orbit altitude for systems that meet the specified performance
requirements listed in Table 5-1. The blue curve represents the predictions using the USCM &
NICM cost models, and the red curve represents the predictions using SSCM. The dotted lines
are values extrapolated using the CERs in the cost models, which lie outside the recommended
range of input masses. Despite being outside the specified ranges, as can be seen by the 2 curves,
the extrapolations still correlate very well to values that lie in the specified ranges, and to the
other models. According to Aerospace Corporation [Mahr and Richardson, 2002], these
extrapolations are less certain but not an unreasonable estimate. Bearden [1996] gives an in-
86

depth analysis of this using the planetary spacecraft NEAR (Near Earth Asteroid Rendezvous),
which went beyond the SSCM database range in several cases, and provided decent correlation
between the model results and the actual spacecraft costs.
The total mission cost in this figure signifies the total cost to design, fabricate, and launch over
the 8-year mission. This cost includes all the units that need to be built throughout the mission
lifetime, and includes learning curves and amortization of costs. Operations & maintenance costs
have not been added to this initial study, but it is predicted that adding this element will not
affect the relative results of the study. Figure 5-1 suggests that multiple small satellites operating
at lower altitudes have the potential to dramatically reduce space mission cost. The objective of
this research is to quantify and validate this hypothesis. There have been many assumptions
made to produce the results of this PBCM. However, changing the values of these assumptions
does not change the shape of the curves in Figure 5-1. That is, the relationship between mission
cost and altitude remained the same over a very wide range of assumed inputs because the shape
of these curves depends only on physics and the empirical mass-based cost models.
The next section will involve a continuation of exploring other performance requirement sets to
obtain and cover a wide and complete range of mission scenarios.

87

6 Research Results and Conclusions
The baseline mission I used for demonstration is presented in Section 5 of this dissertation.
Although the baseline mission makes it clear that there is a high potential for cost reduction for
multiple small satellites flown at lower altitudes for fixed performance requirements, more cases
need to be studied. Firstly, I identified a list of standard and realistic performance requirements
that covers a broad range of Earth Observation missions. This task can be done by first
categorizing each of the performance requirements by “low”, “average”, and “high”
performance. Since I worked with three requirements (resolution, coverage rate, and mission
life-time), there are 27 different combinations that I can study (e.g., high resolution, average
coverage rate, low mission lifetime).
Second, the general PBCM process was performed for each selected set of performance
requirements. For each set of performance requirements selected, values are assigned to each
requirement (e.g., resolution = 2 m, coverage rate = 50,000 km
2
/s, mission life-time = 4 years). I
ran through the PBCM model, as described in Sec. 4, for each set of requirements and determine
the relationship between mission cost and orbit altitude.
Next, I tested and evaluated each individual result against the research hypothesis. Figure 5-1
gives an example plot of mission cost vs. orbit altitude for a typical set of performance
requirements for an Earth observation mission. It has been hypothesized that the shape of the
curve, i.e., the concavity near the lower altitudes will remain despite the fluctuating
requirements. Each set of performance requirements will be tested against the research
hypothesis. The outliers are identified and documented.
88

Finally, I analyzed all the results collectively and individual results as a whole. This is meant to
verify whether the research hypothesis is validated or not completely true. In the event that the
hypothesis does hold true for every set, I identified the cases in which the hypothesis is invalid.

6.1. Results
The 27 scenarios are shown in Table 6-1 with selected values for each performance requirement.
Low (or minimal) performance is indicated by the yellow cells. Average (medium) performance
is indicated by the blue cells. And high performance is indicated by the pink cells. Table 6-1
shows the six scenarios that will be presented, which are representative of typical Earth
observation missions from University studies to Intelligence communities.
I ran through the process as described in Sec. 4 for each set of requirements and determine the
relationship between mission cost and orbit altitude. Next, I tested and evaluated each individual
result against the research hypotheses. Figure 5-1 in Sec. 5 gives an example mission cost vs.
orbit altitude curve for a typical set of performance requirements for an Earth observation
mission. It has been hypothesized that the shape of the curve, i.e., the concavity near the lower
altitudes will remain despite the fluctuating requirements. Each set of performance requirements
were tested against the research hypothesis. Lastly the results are analyzed collectively. Figure 6
1 through Figure 6-6 show the Mission Cost vs. Orbit Altitude curves for each of the scenarios
selected in Table 6-2.
89

Table 6-1. List of 27 Different Performance Requirement Scenarios

Table 6-2. Requirements and Sample Missions for Selected Scenarios

Figure 6-1. Mission Cost vs. Orbit Altitude for Scenario 1
Scenario 1 2 3 4 5 6 7 8 9
Resolution (m) 5 5 5 5 5 5 5 5 5
Coverage (km2/sec) 5,000 5,000 5,000 14,217 14,217 14,217 50,000 50,000 50,000
Lifetime (yrs) 1 4 8 1 4 8 1 4 8
Scenario 10 11 12 13 14 15 16 17 18
Resolution (m) 2 2 2 2 2 2 2 2 2
Coverage (km2/sec) 5,000 5,000 5,000 14,217 14,217 14,217 50,000 50,000 50,000
Lifetime (yrs) 1 4 8 1 4 8 1 4 8
Scenario 19 20 21 22 23 24 25 26 27
Resolution (m) 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5
Coverage (km2/sec) 5,000 5,000 5,000 14,217 14,217 14,217 50,000 50,000 50,000
Lifetime (yrs) 1 4 8 1 4 8 1 4 8
Sample Mission University Weather Science Commercial Defense Intelligence
Scenario 1 8 14 18 24 27
Resolution (m) 5 5 2 2 0.5 0.5
Coverage (km2/sec) 5,000 50,000 14,217 50,000 14,217 50,000
Lifetime (yrs) 1 4 4 8 8 8
90

Figure 6-2. Mission Cost vs. Orbit Altitude for Scenario 8

Figure 6-3. Mission Cost vs. Orbit Altitude for Scenario 14
91

Figure 6-4. Mission Cost vs. Orbit Altitude for Scenario 18

Figure 6-5. Mission Cost vs. Orbit Altitude for Scenario 24
92

Figure 6-6. Mission Cost vs. Orbit Altitude for Scenario 27

6.2. Sample Point Design
To provide a design check, I selected scenario 14 to perform a point design. Scenario 14 is the
case where all three performance requirements are in the “average” category. That is, resolution
required is 2 meters, required coverage rate is 14,200 km
2
/s, and required mission lifetime is 4
years. This scenario was regarded as a “Science” mission in Sec. 6.1. The optimal orbit altitude
for scenario 14 is approximately 400 km, based on the specified performance requirements given
Table 6-2. Traditionally, we might see a system like this designed to fly at 800 km altitude. I
compared these two mission designs with each other.
Table 6-3 shows a mass breakdown for a theoretical spacecraft designed for scenario 14 at 400
km and 800 km for comparison, based on the mass estimation process discussed in Sec. 4.2. The
93

spacecraft subsystem masses were estimated with the help of Table 4-2. As can be inferred, this
spacecraft is only slightly larger than a typical 3U CubeSat at the 400 km altitude case. If this
spacecraft were to be designed, it could be very similar to a 3U CubeSat with an added chemical
propulsion system. To give a visual sense of this type of design, Fig. 6-7 shows a flock of Planet
Labs CubeSats, and Fig. 6-8 shows a new propulsion system being designed for CubeSats by
Aerojet RocketDyne. Of course, affordable small satellites need affordable propulsion systems as
well [Sellers, Paul, and Sweeting, 1998].
Table 6-3. Mass Estimates for Theoretical Spacecraft Designed for Scenario 14 at 400 and 800 km

Subsystems and Cost Parameters 400 km 800 km Unit
Payload 1.24 7.55 kg
Required Aperture Diameter 11 22 cm
Spacecraft Bus 2.76 16.80 kg
Structure 0.97 5.88 kg
Power System 0.58 3.53 kg
ADCS 0.17 1.01 kg
On-Board Processing 0.14 0.84 kg
Propulsion System 0.08 0.50 kg
Thermal Control 0.06 0.34 kg
TT&C 0.06 0.34 kg
Propellent 2.20 0.03 kg
Total Mass 6.20 24.38 kg
Mission Cost (USCM+NICM estimate) $26.26 $54.56 FY13$M
Mission Cost (SSCM estimate) $3.62 $7.37 FY13$M
Average Mission Cost $14.94 $30.97 FY13$M
94

Figure 6-7. Flock of Planet Labs
*
CubeSats

Figure 6-8. MPS-130™ CubeSat High-Impulse Adaptable
†

*
Planet Labs Inc. 2016. Website: https://www.planet.com/
†
Aerojet Rocketdyne. 2016. MPS-130™ CubeSat High-Impulse Adaptable. Website: https://www.rocket.com/cubesat/mps-130
95

6.3. Conclusions
The ever-increasing cost of space missions leads to longer schedules and fewer missions. This
leads to a demand for higher reliability, which, in turn, leads to higher cost, longer schedules,
and fewer missions. This process is known as the space spiral. Space missions today cost too
much, and within today’s budget environment, it has become a national problem. Small
commercial startups companies such as Skybox Imaging and Planet Labs have broken this trend
and are now designing and flying smaller low-cost spacecraft at lower altitudes to provide world-
wide imaging capabilities with slightly lower performance (resolution). My research explored
various sets of performance requirements for Earth observation missions and quantifies the effect
orbit altitude has on total mission cost, in low Earth orbit.
Before this research, there were no known studies that quantify the effect orbit altitude has on
mission cost. It is known that it is lower cost to design smaller satellites than larger satellites, and
lower cost to launch satellites into a lower (rather than a higher) Earth orbit. But no studies
quantified how and if small, low-cost satellites relative to larger traditional satellites can provide
the same level of performance and meet the requirements of a mission. This research
successfully created this information.
Section 2.2 described my research objectives and the method of achieving these goals have been
discussed in detail from Sec. 4 on. Performing a literature review (Sec. 3) allowed me to find gaps
in current approaches to determining the relationship between orbit altitude and cost. This process
also allowed me to find the tools and methods necessary to initiate my research and create the
performance-based cost modeling (PBCM) approach. I developed a systems engineering approach
to quantify the relationship between mission cost vs. orbit altitude for Earth observation missions
given a fixed level of performance (resolution, coverage, and lifetime). Using existing cost
96

models and system engineering, I was able to formulate a process that determines the relationship
between cost and performance over a range of orbit altitudes. The advantage of PBCM is that it
takes an interdisciplinary approach that utilizes physics, systems engineering, the use of existing,
well-established, cost models, and incorporates common cost estimating techniques. My goal
was not to create a new cost model, but to use existing, accepted cost models in a new way.
I found preliminary results and formulated a research hypothesis with respect to reducing space
mission cost. I presented the approach and preliminary research at conferences and workshops to
introduce the concept to the aerospace and cost modeling community to gain feedback and peer-
review. I presented my research at several aerospace, systems engineering, and cost modeling
conferences such as the 2013 AIAA Reinventing Space Conference, AIAA Space 2014
Conference, 2014 BIS Reinventing Space Conference, and the 2016 AIAA Science and
Technology Conference, and a ICEAA Workshop in 2013. After a great deal of feedback and
review from professionals at these workshops and conferences, I identified sets of realistic
performance requirements for Earth observation missions and tested the hypotheses on each case
using the formulated approach. Finally, I analyzed and evaluated the results collectively to
validate the hypothesis, and also identified unforeseen results that are all useful and beneficial to
the aerospace community.
In Sec. 5.1, I discussed my research hypotheses which are:
1. There will be a concavity in the total mission cost vs. orbit altitude curve.
2. The optimal altitude for Earth observation missions will be at or below 500 km regardless
of performance requirements.
97

3. Flying multiple small satellites at lower altitudes than traditional systems, Earth
observation satellites can achieve much lower overall mission costs.
As can be seen from Figure 6-1 through Figure 6-6, there exists a concavity in each scenario.
The same holds for all 27 cases. Appendix 7.3 shows the relationships for all 27 scenarios. The
green arrow in each of these figures signifies the median point of concavity in each curve, i.e.,
the most optimal orbit altitude regime in terms of mission cost determined using both
USCM/NICM and SSCM. My prediction for hypothesis 1 is correct.
The basic reason behind the existence of the concavity is two-fold. Below roughly 200 km, the
density of the atmosphere reaches a point where spacecraft orbiting at these altitudes require very
large amounts of propellant to stay in orbit due to drag. This combined with the greater number
of spacecraft needed at lower altitudes (below 200 km) increases launch cost dramatically and
therefore increases overall mission costs. This happens in every scenario. Conversely, above 200
km, the atmosphere becomes less dense such that spacecraft require less and less propellant to
stay in orbit to counteract drag. Less propellant required onboard means lower spacecraft mass,
and therefore lower overall mission cost. Secondly, as discussion in Sec. 5.1, if we reduce the
altitude by a factor of 2, we will also reduce the instrument aperture and linear dimensions of the
spacecraft by a factor of 2. For spacecraft with similar densities, this reduces the volume and mass
of the spacecraft by a factor of 8. Moreover, reducing the altitude by a factor of 4 can reduce the
mass and volume by a factor of 64. Since Cost Estimating Relationships (CERs) in cost models are
strictly increasing functions, larger spacecraft are simply projected to cost more. The overall cost
of building larger and larger systems outweigh the overall cost of building multiple relatively
smaller systems. Hence, the increase in mission cost as altitude is increased.
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Of the 27 different scenarios, only 6 have an optimal orbit altitude above 500 km, and therefore
the second research hypothesis is not entirely correct. These counter-examples include scenario’s
4–9. All 6 of these cases have a common theme – a combination of low (minimal) resolution
requirement and moderate-to-high coverage requirement. Of these 6 scenarios, which fall under
the low (minimal) resolution requirement, none of them require large spacecraft masses at all.
This is due to the fact that a low resolution requirement will only need a small, light-weight
sensor. It should be noted that small spacecraft flown at higher altitudes can also meet mission
requirements. All 21 other scenarios have optimal orbit altitudes at or below 500 km. On the
average, the optimal orbit altitudes of all 27 scenarios lie within the 425–500 km range as can be
seen in Table 6-4. The pink cells highlight the optimal altitudes above 500 km, and the blue and
yellow cells highlight ones at or below 500 km.
The third hypothesis has also been quantifiably confirmed as a result of this study. The results
have shown that flying at lower altitudes has its advantages especially for moderate-to-high
resolution requirements. Of course, these lower limits are reached once launch costs become a
major factor for spacecraft requiring larger amounts of propellant for orbit maintenance. If the
optimal altitude point can be found early in mission design, there will be much higher potential
for cost savings. The highest potential for cost reduction is observed in missions with high
(better) resolution requirements.

99

Table 6-4. Optimal Altitude Determined for Each Scenario

From the foregoing, we can conclude that multiple small satellites flown at lower altitudes (or
sometimes higher altitudes) can reduce the cost of Earth observation missions by a factor of 2–10
relative to traditional large systems while meeting the same performance and mission lifetimes.
This is largely the case for all Earth observation missions with one exception; missions that
combine both low (minimal) resolution requirements and high coverage requirements, which can
also save cost by flying small spacecraft at higher altitudes. Implications of this study is
discussed in Sec. 6.4.

USCM/NICM SSCM Median Lifetime Coverage Resolution
1 350 500 425 Low
2 350 500 425 Moderate
3 500 500 500 High
4 800 800 800 Low
5 700 700 700 Moderate
6 800 800 800 High
7 800 1000 900 Low
8 800 1000 900 Moderate
9 900 1000 950 High
10 300 350 325 Low
11 300 400 350 Moderate
12 350 400 375 High
13 350 400 375 Low
14 400 400 400 Moderate
15 400 500 450 High
16 350 500 425 Low
17 400 500 450 Moderate
18 500 500 500 High
19 200 200 200 Low
20 200 250 225 Moderate
21 250 250 250 High
22 200 250 225 Low
23 250 250 250 Moderate
24 250 250 250 High
25 200 250 225 Low
26 250 300 275 Moderate
27 300 300 300 High
Average 424 483 454
High
Performance Requirements Optimum Orbit Altitude (km)
Low
(Minimal)
Moderate
High
Low
Moderate
High
Low
Moderate
High
Low
Scenario
Moderate
100

6.4. Implications of Research
The completion of this research will positively impact the way business is done in space and
effectively start reversing the space spiral as shown in Figure 6-9. When missions are lower cost,
risk is reduced and the demand for high reliability is also reduced. This in turn, allows program
to have shorter schedules and more missions can be created. Anything that reduces cost, aids in
reversing the space spiral.

Figure 6-9. Reversing the Space Spiral
The key implication of this research is that we can build and operate spacecraft at lower
altitudes, accept a shorter lifetime, and gain the benefits of a different operating concept. If it is
possible to provide required performance levels by using smaller, (shorter-lived), satellites at
lower altitudes, the possibility of using smaller, launch-on-demand boosters also becomes more
feasible [Eves, 2013].
Using modern microelectronics, future SmallSat observation systems at a lower altitude than
traditional systems can have:
• Much lower overall mission cost
• Comparable or better performance (resolution and coverage)
101

• Lower risk (both implementation and operations)
• Shorter development schedules
Some relevant secondary advantages for the low-altitude SmallSats include:
• Lower up-front development cost
• More sustainable business model
• More flexible and better mission/system resilience (see Neches and Madni [2014], Madni
and Jackson [2009], and Madni and Boehm [2014])
• More responsive to both evolving technologies and changing needs
• Mitigates the problem of orbital debris
One of the biggest challenges is changing the way we do business in space and how we think
about using space systems. In order to fully take advantage of these findings, responsive, low-
cost, small launch systems for operational missions are needed. It does not make sense to launch
a $10M spacecraft on a $50M+ launch vehicle. The cost of the spacecraft and launch vehicle
should be comparable.
Below is a list of recommendations for practitioners for Earth observation missions:
• When possible, design spacecraft for low altitudes which allow you to take advantage of
smaller, lighter weight sensors
• Take advantage of existing and advancing microelectronics (e.g., COTS CubeSat
components) in order to drive down system mass and cost
• Develop low cost agile launch vehicles dedicated for small satellites

102

6.5. Contribution to Society
My research has several positive benefits for society at large. My finding can help programs
create lower cost solutions for Earth observation missions. In addition, this research establishes
the ability of small, low altitude satellites to meet real mission requirements. The ability for
the small satellite community to enter the space industry beyond just university studies will
increase. I am optimistic and hopeful that this research will encourage the community to
undertake further research on other complementary ways to reduce space mission costs.
After telecommunications, remote sensing of the Earth’s surface, oceans, and atmosphere has
long been identified as one of the most promising areas for the commercialization of space.
Remote sensing has a very high level of military utility as well. There can be no doubt that the
extremely high cost of existing Earth observation satellites has a significant influence on the
price of data. [Fouquet and Ward, 1998] This research will help missions providing Earth
observations make better decisions in terms of cost. Especially today, when budget cuts are a
common problem in the U.S., it is crucial to find ways to respond to global needs in an
affordable manner without canceling other important programs. If we can find ways to
dramatically reduce the cost of space missions while still meeting global needs, this will allow us
to do more (with less), in space. My research is a key step in this direction.
Lower cost satellites can potentially lead to lower cost launch vehicles, which in turn, could lead
the U.S. to have launch-on-demand. Many benefits await the U.S. if it develops launch-on-
demand. We could dedicate special satellites for monitoring and providing communications to
our forces in remote locations and could replace satellites lost to accidents or interference
[Cooper, 1992]. Launch-on-demand is currently a priority for the Department of Defense (DoD).
For example, DARPA has been developing new approaches to launching satellites into orbit on
103

short notice and at low cost which have the potential to enable launch of satellites from virtually
anywhere with just 24 hours’ notice and at a fraction of current costs
*
[Space News, 2015]. With
launch-on-demand, the U.S. will be able to drive down the cost of launch vehicle boosters. By
driving down the cost of satellites combined with the use of inexpensive boosters, DoD and
NASA will be able to afford more orbital assets, even if their space budgets remain flat or
decrease somewhat. Lower cost spacecraft and inexpensive launch vehicles can potentially
generate a commercial satellite boom, and an increase in industrial demand for spacecraft could
create opportunities for significant manufacturing economies of scale [London, 1994].
In addition, my research findings can potentially be a valuable contribution to the field of space
systems cost modeling, and space systems engineering. The positive result from this research
provides an alternative way for engineers and program managers to use existing cost models to
make more informed systems engineering choices early in mission design. This research will
allow existing cost models to not only just predict cost, but to be a more valuable tool to all
users. In addition, this research can potentially introduce industry to alternate ways of mission
design at a systems engineering level. In particular, the Performance Based Cost Modeling
approach can be applied to many other types of missions.

6.6. Recommendations for Future Work
The Performance-Based Cost Modeling approach may be useful in determining relationships
between Mission Cost vs. Orbit Altitude for other types of missions including but not limited to

*
Space News. 2015. “DARPA Space Efforts Address U.S. Reliance on Space.” March 30.
104

communication systems, passive systems (e.g., radar, lidar), and Global Positioning Systems
(GPS) or Global Navigation Satellite Systems (GNSS).
Some factors to consider as an extension to this research may include:
• Operations cost
• Verify higher orbits such as upper LEO, MEO, GEO
• Orbit inclinations and their limitations
• Repeat or revisit rates of satellites
• Exploring various propulsion systems (see Sellers, Paul, and Sweeting [1998])
Although a learning curve approach was applied for the build of multiple units in the approach
for estimating mission costs, there may be ways to further model cost savings resulting from this
approach. New technologies employed on by missions with shorter design life may have the
advantages of cheaper, faster, lower power, lighter components. Based on the conclusions, it may
be relevant to seek the capacity to accommodate evolving technologies.
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7 Appendix
7.1. Selected Data for Past Spacecraft
The following table tabulates the data used to produce the results presented in Sec. 3.4.2 for the
average duration for development of large and small satellites.
Table 7-1. Authorization to Proceed (ATP) Date, Launch Date, and Size of Spacecraft.

Program
Dry Weight
(kg)

Size

ATP

Launch
Duration
(Months)
Duration
(Years)
Lunar Orbiter 1 387 Small 11/1/1964 8/10/1966 23.1 1.8
ATS-1 Applications
Technology Sat
352 Small 2/1964 12/7/1966 37.1 2.8
IMP-H (IMP-6) 288 Small 6/1/1968 3/13/1971 36.3 2.8
SMS-1 583.6 Small 12/1/1970 5/17/1974 45.1 3.5
S3 154.5 Small 6/1/1972 10/15/1974 30.9 2.4
Voyager 1 815 Small 2/1/1972 9/5/1977 73.0 5.6
AEM-HCMM 134 Small 12/1/1974 4/26/1978 44.4 3.4
P78 463.6 Small 1/1974 2/24/1979 67.1 5.2
MAGSAT 181 Small 4/1/1977 10/30/1979 33.6 2.6
SME 437 Small 1/1/1977 10/6/1981 62.1 4.8
UoSAT-1 52 Small 9/1/79 10/6/81 63.8 2.1
UoSAT-2 60 Small 9/1/83 3/1/84 15.2 0.5
AMPTE 77-705 Small 2/1/1982 8/16/1984 33.1 2.5
ERBS 226 Small 1/1/1981 10/5/1984 49.0 3.8
Galileo Probe 339 Small 2/1/1978 10/18/1989 152.8 11.7
UoSAT-3 45.5 Small 7/1/88 1/22/90 47.5 1.6
UoSAT-4 47.5 Small 7/1/88 1/22/90 47.5 1.6
Ulysses 367 Small 10/1/1978 10/6/1990 156.7 12.0
UoSAT-5 48.4 Small 5/1/90 7/17/91 36.8 1.2
KITSAT-1 48.6 Small 7/1/91 8/10/92 33.8 1.1
S80/T 50 Small 8/1/91 8/10/92 31.3 1.0
HealthSat-2 44 Small 9/1/92 9/26/93 32.5 1.1
PoSAT-1 49 Small 6/1/92 9/26/93 40.2 1.3
KitSAT-2 48.7 Small 8/1/92 9/26/93 35.1 1.2
Clementine 228 Small 2/15/1992 1/25/1994 25.4 1.9
CERISE 50 Small 9/1/91 7/7/95 117.1 3.9
FASAT-Alpha 55 Small 5/1/94 8/31/95 40.6 1.3
TOMS-EP 210.0 Small 2/1/1991 7/2/1996 70.6 5.4
FAST 183.6 Small 10/1/1991 8/12/1996 63.5 4.9
106

Program
Dry Weight
(kg)

Size

ATP

Launch
Duration
(Months)
Duration
(Years)
HETE / SAC-B 128 Small 4/1/92 11/4/96 59.9 4.6
MGS 75.9 Small 2/15/1994 11/7/96 35.6 2.7
ACE 545.5 Small 10/1/1993 8/25/1997 50.9 3.9
Lunar Prospector 158 Small 4/1/1995 1/6/1998 36.1 2.8
FASAT-Bravo 55 Small 5/1/96 7/10/98 66.7 2.2
TMSAT 55 Small 11/1/95 7/10/98 81.8 2.7
Deep Space 1 373 Small 10/1/1995 10/24/1998 40.0 3.1
SWAS 282.4 Small 6/1/1994 12/5/1998 58.9 4.5
STARDUST 299.4 Small 12/1/1995 2/7/1999 41.6 3.2
WIRE 255.5 Small 1/1/1995 3/4/1999 54.4 4.2
UoSAT-12 312 Small 8/1/94 4/21/99 143.7 4.7
Clementine 50 Small 10/1/96 12/3/99 96.5 3.2
IMAGE 470.9 Small 10/1/1996 3/25/2000 45.4 3.5
Tsinghua-1 49.7 Small 12/1/98 6/28/00 47.9 1.6
SNAP-1 8.3 Small 9/1/99 6/28/00 25.1 0.8
TiungSat 50.8 Small 6/4/97 9/26/00 100.8 3.3
EO-1 556.3 Small 3/1/1996 11/21/2000 61.6 4.7
Genesis 494 Small 11/1/1997 8/8/2001 49.1 3.8
PICOSAT 67.2 Small 5/1/97 10/1/01 134.5 4.4
SAGE III 31.4 Small 1/1/95 12/10/2001 90.5 6.9
GRACE 457.7 Small 3/17/2002 57 4.8
CONTOUR 387 Small 10/1/1997 7/3/2002 62.0 4.8
ALSat 90.3 Small 11/15/00 11/28/02 61.9 2.0
GALEX 287.1 Small 4/1/1999 4/28/2003 53.1 4.1
SIRTF or Spitzer 357.3 Small 1/1/1996 8/25/2003 99.8 7.7
NiSAT-1 30.1 Small 1/15/01 9/27/03 82.1 2.7
UK-DMC 30.1 Small 1/30/01 9/27/03 80.8 2.7
BILTEN Tubitek 130.1 Small 8/12/01 9/27/03 64.7 2.1
MESSENGER 507.9 Small 12/1/1999 8/3/04 61.0 4.7
DART 360 Small 6/1/2001 4/15/2005 50.5 3.9
TOPSAT 113.8 Small 7/25/00 10/28/05 160.1 5.3
DMC+China 163.6 Small 2/19/03 10/28/05 81.8 2.7
Giove-A 649 Small 7/11/03 10/28/05 70.0 2.3
CFESAT Small 9/27/03 3/30/06 76.3 2.5
CALIPSO 548.4 Small 12/1/1998 4/28/2006 96.6 7.4
RapidEye-1a 163 Small 7/14/04 3/9/07 80.7 2.7
GRAIL 133 Small 12/1/2007 9/10/2011 49.3 3.8
POPACS 2.00 Small 9/30/13 18 1.5
HEAO-1 2552 Large 5/1/1972 8/12/1977 68.9 5.3
HEAO-2 3130 Large 5/1/1972 11/13/1978 85.3 6.5
107

Program
Dry Weight
(kg)

Size

ATP

Launch
Duration
(Months)
Duration
(Years)
Shuttle Orbiter 99117 Large 8/9/1972 4/12/1981 113.1 8.7
COBE 2206.0 Large 1/1/1977 11/18/1989 168.0 12.9
Hubble Space
Telescope
11110 Large 10/1/1977 4/25/1990 163.9 12.6
ROSAT 2462 Large 1/15/1982 6/1/1990 109.3 8.4
CRRES 4383 Large 2/1/1983 7/25/1990 97.5 7.5
GRO / CGRO 13531.8 Large 1/1/1978 4/5/1991 172.9 13.3
UARS 5584.5 Large 3/1/1985 9/12/1991 85.2 6.5
Topex/Poseidon 2148.2 Large 10/1/1986 8/10/1992 76.4 5.9
ACTS 2,767 Large 8/1/1984 9/1/1993 118.5 9.1
Cassini 2700 Large 1/1/1990 10/15/1997 101.6 7.8
TRMM 2610.9 Large 10/1/1990 11/27/1997 93.4 7.2
Chandra 4800 Large 1/1/1977 7/23/1999 294.2 22.6
TERRA (AM-1) 4405.5 Large 1/1/1991 12/18/1999 116.9 9.0
Aqua (PM-1) 2798.6 Large 8/1/1993 5/4/2002 114.2 8.8
WGS Large 10/10/07 83 6.9
GOES O 3,133 Large 1/28/98 6/27/2009 148.9 11.4
GOES P 3133 Large 1/28/98 3/4/2010 157.8 12.1
GPS IIF Large 5/1/10 123 10.3
AEHF Large 8/14/10 107 8.9
SBIRS High 4500 Large 5/7/11 175 14.6
MSL 4050 Large 5/1/2005 11/26/2011 85.7 6.6

108

7.2. Variation in Input Assumptions
Figure 7-1 shows us Mission Cost vs. Orbit Altitude as a function of learning curve. As can be
seen by the figure, the relative relationship of the curve does not change much. That is, there is
still a concavity in the curve that tells us that there is an optimum cost point closer to the lower
altitudes (< 300 km) for this set of performance requirements.

Figure 7-1. Mission Cost vs. Orbit Altitude Over a Range of Learning Curves

109

7.3. Mission Cost vs. Orbit Altitude for All 27 Scenarios
Figure 7-2 through Figure 7-28 show the Mission Cost vs. Orbit Altitudes graphs for all 27
scenarios discussed in Section 6.

Figure 7-2. Mission Cost vs. Orbit Altitude for Scenario 1
110

Figure 7-3. Mission Cost vs. Orbit Altitude for Scenario 2

Figure 7-4. Mission Cost vs. Orbit Altitude for Scenario 3
111

Figure 7-5. Mission Cost vs. Orbit Altitude for Scenario 4

Figure 7-6. Mission Cost vs. Orbit Altitude for Scenario 5
112

Figure 7-7. Mission Cost vs. Orbit Altitude for Scenario 6

Figure 7-8. Mission Cost vs. Orbit Altitude for Scenario 7
113

Figure 7-9. Mission Cost vs. Orbit Altitude for Scenario 8

Figure 7-10. Mission Cost vs. Orbit Altitude for Scenario 9
114

Figure 7-11. Mission Cost vs. Orbit Altitude for Scenario 10

Figure 7-12. Mission Cost vs. Orbit Altitude for Scenario 11
115

Figure 7-13. Mission Cost vs. Orbit Altitude for Scenario 12

Figure 7-14. Mission Cost vs. Orbit Altitude for Scenario 13
116

Figure 7-15. Mission Cost vs. Orbit Altitude for Scenario 14

Figure 7-16. Mission Cost vs. Orbit Altitude for Scenario 15
117

Figure 7-17. Mission Cost vs. Orbit Altitude for Scenario 16

Figure 7-18. Mission Cost vs. Orbit Altitude for Scenario 17
118

Figure 7-19. Mission Cost vs. Orbit Altitude for Scenario 18

Figure 7-20. Mission Cost vs. Orbit Altitude for Scenario 19
119

Figure 7-21. Mission Cost vs. Orbit Altitude for Scenario 20

Figure 7-22. Mission Cost vs. Orbit Altitude for Scenario 21
120

Figure 7-23. Mission Cost vs. Orbit Altitude for Scenario 22

Figure 7-24. Mission Cost vs. Orbit Altitude for Scenario 23
121

Figure 7-25. Mission Cost vs. Orbit Altitude for Scenario 24

Figure 7-26. Mission Cost vs. Orbit Altitude for Scenario 25
122

Figure 7-27. Mission Cost vs. Orbit Altitude for Scenario 26

Figure 7-28. Mission Cost vs. Orbit Altitude for Scenario 27
123

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Abstract (if available)

Abstract The ever-increasing cost of space missions inevitably leads to longer schedules and fewer missions. This leads to a demand for higher reliability, which, in turn, leads to higher cost, longer schedules, and fewer missions. This process is known as the space spiral. Space missions today are costing too much, and within today’s budget environment, it has become a national problem. In recent times, small commercial startups companies such as Skybox Imaging and Planet Labs have broken this trend and have been designing and flying smaller low-cost spacecraft at lower altitudes to provide world-wide imaging capabilities

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Asset Metadata

Creator Shao, Anthony (author)

Core Title Quantifying the effect of orbit altitude on mission cost for Earth observation satellites

School Viterbi School of Engineering

Degree Doctor of Philosophy

Degree Program Astronautical Engineering

Publication Date 04/21/2016

Defense Date 01/20/2016

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag cost models,Earth observation satellites,mission cost vs. altitude,OAI-PMH Harvest,orbit altitude,performance requirements,reducing space mission cost,reinventing space,resolution,SmallSats

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Erwin, Daniel A. (committee chair), Madni, Azad M. (committee chair), Autry, Gregory W. (committee member), Boehm, Barry W. (committee member), Kunc, Joseph A. (committee member), Wertz, James R. (committee member)

Creator Email ant.shao@gmail.com,shaoa@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-238772

Unique identifier UC11278533

Identifier etd-ShaoAnthon-4335.pdf (filename),usctheses-c40-238772 (legacy record id)

Legacy Identifier etd-ShaoAnthon-4335.pdf

Dmrecord 238772

Document Type Dissertation

Format application/pdf (imt)

Rights Shao, Anthony

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

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