Title :

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

Author(s) :

Nguyen, Thi Le Thu [Auteur]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Rajaona, Harizo [Auteur]
DAM Île-de-France [DAM/DIF]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Peters, Gareth W. [Auteur]
Nevat, Ido [Auteur]
Institute for Infocomm Research - I²R [Singapore]
Delignon, Yves [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]

Journal title :

IEEE Transactions on Signal Processing

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2016

ISSN :

1053-587X

English keyword(s) :

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

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

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

Source :

Harvested from HAL