A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

Thi Le Thu Nguyen; François Septier; Harizo Rajaona; Gareth W. Peters; Ido Nevat; Yves Delignon

doi:10.1109/TSP.2015.2505689

Journal articles

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

Thi Le Thu Nguyen ¹ François Septier ^{2, 3} Harizo Rajaona ^{2, 4} Gareth W. Peters ⁵ Ido Nevat ⁶ Yves Delignon ^{2, 3}

1 VNU HCM - Depart. Probability and Statistics, HCM VNU

2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

3 Institut TELECOM/TELECOM Lille1

4 DAM/DIF - DAM Île-de-France

5 Department of Statistical Science, University College of London

6 Institute for Infocomm Research - I²R [Singapore]

Abstract : In this paper we address the challenging problem of multiple source localization in Wireless Sensor Networks (WSN). We develop an efficient statistical algorithm, based on the novel application of SequentialMonte Carlo (SMC) sampler methodology, that is able to deal with an unknown number of sources given quantized data obtained at the fusion center from different sensors with imperfect wireless channels. We also derive the Posterior Cram´er-Rao Bound (PCRB) of the source location estimate. The PCRB is used to analyze the accuracy of the proposed SMC sampler algorithm and the impact that quantization has on the accuracy of location estimates of the sources. Extensive experiments show the benefits of the proposed scheme in terms of the accuracy of the estimation method that is required for model selection (i.e., the number of sources) and the estimation of the source characteristics compared to the classical importance sampling method.

Keywords : Bayesian in- ference Sequential Monte Carlo sampler multiple sources quantized data Wireless sensor networks localization

Document type :

Journal articles

Domain :

Engineering Sciences [physics] / Signal and Image processing

Statistics [stat] / Applications [stat.AP]

Statistics [stat] / Computation [stat.CO]

Statistics [stat] / Methodology [stat.ME]

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https://hal-imt.archives-ouvertes.fr/hal-01238931
Contributor : François Septier <>
Submitted on : Monday, December 7, 2015 - 12:22:09 PM
Last modification on : Friday, November 27, 2020 - 2:20:03 PM

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

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