DocumentCode :
29338
Title :
Cooperative Localization in WSNs Using Gaussian Mixture Modeling: Distributed ECM Algorithms
Author :
Feng Yin ; Fritsche, Carsten ; Di Jin ; Gustafsson, Fredrik ; Zoubir, Abdelhak M.
Author_Institution :
Signal Process. Group, Tech. Univ. Darmstadt, Darmstadt, Germany
Volume :
63
Issue :
6
fYear :
2015
fDate :
15-Mar-15
Firstpage :
1448
Lastpage :
1463
Abstract :
We study cooperative sensor network localization in a realistic scenario where the underlying measurement errors more probably follow a non-Gaussian distribution; the measurement error distribution is unknown without conducting massive offline calibrations; and non-line-of-sight identification is not performed due to the complexity constraint and/or storage limitation. The underlying measurement error distribution is approximated parametrically by a Gaussian mixture with finite number of components, and the expectation-conditional maximization (ECM) criterion is adopted to approximate the maximum-likelihood estimator of the unknown sensor positions and an extra set of Gaussian mixture model parameters. The resulting centralized ECM algorithms lead to easier inference tasks and meanwhile retain several convergence properties with a proof of the “space filling” condition. To meet the scalability requirement, we further develop two distributed ECM algorithms where an average consensus algorithm plays an important role for updating the Gaussian mixture model parameters locally. The proposed algorithms are analyzed systematically in terms of computational complexity and communication overhead. Various computer based tests are also conducted with both simulation and experimental data. The results pin down that the proposed distributed algorithms can provide overall good performance for the assumed scenario even under model mismatch, while the existing competing algorithms either cannot work without the prior knowledge of the measurement error statistics or merely provide degraded localization performance when the measurement error is clearly non-Gaussian.
Keywords :
Gaussian distribution; Gaussian processes; approximation theory; computational complexity; cooperative communication; expectation-maximisation algorithm; maximum likelihood estimation; measurement errors; mixture models; radiotelemetry; wireless sensor networks; ECM criterion; Gaussian mixture modeling; WSN; average consensus algorithm; calibration; communication overhead; computational complexity; cooperative sensor network localization; distributed ECM algorithm; distributed algorithm; expectation-conditional maximization criterion; inference task; maximum-likelihood estimator; measurement error distribution; non-line-of-sight identification; nonGaussian distribution; scalability requirement; space filling condition; Approximation algorithms; Electronic countermeasures; Inference algorithms; Maximum likelihood estimation; Measurement errors; Signal processing algorithms; Wireless sensor networks; Centralized and distributed algorithms; Gaussian mixture; cooperative localization; expectation–conditional maximization (ECM); wireless sensor network (WSN);
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2015.2394300
Filename :
7015606
Link To Document :
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