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ROBUST ACOUSTIC SOURCE LOCALIZATION IN SENSOR NETWORKS
by
Chartchai Meesookho
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ELECTRICAL ENGINEERING)
August 2007
Copyright 2007 Chartchai Meesookho
Object Description
| Title | Robust acoustic source localization in sensor networks |
| Author | Meesookho, Chartchai |
| Author email | chartchai@gmail.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Electrical Engineering |
| School | Viterbi School of Engineering |
| Date defended/completed | 2007-06-19 |
| Date submitted | 2007 |
| Restricted until | Unrestricted |
| Date published | 2007-07-29 |
| Advisor (committee chair) |
Narayanan, Shrikanth S. Mitra, Urbashi |
| Advisor (committee member) | Sukhatme, Gaurav S. |
| Abstract | Our goal is to improve the performance of acoustic source localization in the context of sensor networks. A number of relevant problems are addressed and solutions are proposed.; As data is usually collected by sensors with randomly distributed locations, a problem that emerged for source localization is how to efficiently gather all the required data and combine them subject to selected localization schemes. In Chapter 3, we propose a distributed algorithm based on a specific method, range difference based localization. Simulation results illustrate that the distributed localization produces smaller error and consumes less energy than centralized method. The advantage of distributed processing becomes more conspicuous for error considerations when the number of participating sensors is small and obtains more energy saving with the large number of participating sensor. The proposed method is also more robust to decreasing target signal energy and the instantaneous error from the sequence of estimates can be approximated and used to reconcile the cost and the system performance.; Considering recently proposed methods, energy based localization attracts our interest due to their simplicity and the possibility of earning energy saving while maintaining acceptable accuracy. The investigation of existing energy based methods leads to findings that show the possibility of further improvement. In Chapter 4, energy-based localization methods for source localization in sensor networks are examined. The focus is on least squares based approaches due to a good trade-off between performance and complexity. A suite of methods are developed and compared. First, two previously proposed methods (Quadratic Elimination and One Step) are shown to yield the same location estimate for a source. Next, it is shown that as the errors which perturb the least squares equations are non-identically distributed, it is more appropriate to consider weighted least squares methods which is observed to yield significant performance gains over the unweighted methods. Finally, a new weighted direct least squares formulation is presented and shown to outperform the previous methods with much less computational complexity. Unlike the Quadratic Elimination method, the weighted direct least squares method is amenable to a correction technique which incorporates the dependence of unknown parameters leading to further performance gains. For a sufficiently large number of samples, simulations show that the Weighted Direct solution with Correction (WDC) can be more accurate with significantly less computational complexity than the maximum likelihood estimator and approaches Cramér-Rao Bound (CRB). Furthermore, it is shown that WDC attains CRB for the case of a white source.; Since the consideration of system design is inevitably important, in Chapter 5, design rules for sensor network deployment for acoustic source localization are determined. In particular, methods based on time-delay information in the received acoustic signal time series and those based on energy readings are examined through the evaluation of Cramér Rao bounds on the estimation error variance. Assuming unknown source location and nearest neighbor sensor participation in localization, the minimum and the maximum of the CRBs (over source location) for the case of three sensors and the limiting case approximation for large number of sensors are derived. The derived limits, which are functions of key design parameters such as number of sensors, grid size, sampling frequency, and number of collected samples, are shown to be good approximations as validated by direct numerical evaluation of the true CRBs. Experimental simulation results demonstrate that the performances of the Maximum-Likelihood estimators using each observation model are consistent with the corresponding CRB, and thus the derived CRBs can be used for performance prediction. The comparison illustrates that the energy based observation model, generally assumed to be less accurate due to potential information loss in the energy calculation, can be superior for low-frequency sources and small grid size (dense) fields. Design rules, explicitly presented as mathematical expressions, and examples of their application, are provided for system parameter and scheme selection.; In Chapter 6, maximum-likelihood estimation for near-field acoustic source localization is examined. Prior work has focused on localization schemes based on time-delay information solely or received signal energy both of which are a function of the source location. Herein, a signal model which explicitly captures location dependence on signal delay and signal attenuation is developed and a corresponding maximum-likelihood estimator derived (HML). Analysis of achievable performance for time-delay information only, received signal energy only and the hybrid signal models is done via the determination of the Cram´er-Rao Bounds (CRB). The CRB analysis shows that the hybrid scheme offers significant performance improvement over time-delay based methods for low-frequency sources; similarly strong performance gains over the received signal energy model is achieved for high-frequency sources. Simulation results confirm these trends for the associated maximum likelihood estimators and further indicate that source location plays a strong role in determining whether energy or time-delay based schemesare superior relative to each other. Energy based schemes incur a smaller communication cost relative to the other methods, suggesting that HML should always be used over time-delay based methods. However, field experiments with the Acoustic ENSBox show that HML also exhibits significant robustness over energy-based schemes, suggesting that HML should also be considered for low power environments where communication costs can be reduced by reducing the number of observations. |
| Keyword | acoustic; localization; sensor; networks |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m715 |
| Contributing entity | University of Southern California |
| Rights | Meesookho, Chartchai |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Meesookho-20070729 |
| Archival file | uscthesesreloadpub_Volume48/etd-Meesookho-20070729.pdf |
Description
| Title | Page 1 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | ROBUST ACOUSTIC SOURCE LOCALIZATION IN SENSOR NETWORKS by Chartchai Meesookho A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (ELECTRICAL ENGINEERING) August 2007 Copyright 2007 Chartchai Meesookho |
