A Bayesian Perspective on Multiple Source ...
Type de document :
Compte-rendu et recension critique d'ouvrage
DOI :
Titre :
A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks
Auteur(s) :
Nguyen, Thi Le Thu [Auteur]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Rajaona, Harizo [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
DAM Île-de-France [DAM/DIF]
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
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]
Institut TELECOM/TELECOM Lille1
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Institut TELECOM/TELECOM Lille1
Rajaona, Harizo [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
DAM Île-de-France [DAM/DIF]
Peters, Gareth W. [Auteur]
University College of London [London] [UCL]
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]
Institut TELECOM/TELECOM Lille1
Titre de la revue :
IEEE Transactions on Signal Processing
Éditeur :
Institute of Electrical and Electronics Engineers
Date de publication :
2016
ISSN :
1053-587X
Mot(s)-clé(s) en anglais :
Wireless sensor networks
localization
multiple sources
quantized data
Sequential Monte Carlo sampler
Bayesian in- ference
localization
multiple sources
quantized data
Sequential Monte Carlo sampler
Bayesian in- ference
Discipline(s) HAL :
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]
Statistiques [stat]/Applications [stat.AP]
Statistiques [stat]/Calcul [stat.CO]
Statistiques [stat]/Méthodologie [stat.ME]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Vulgarisation :
Non
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