Title :

Gradient based sequential Markov chain Monte Carlo for multi-target tracking with correlated measurements

Author(s) :

Lamberti, Roland [Auteur]
Centre National de la Recherche Scientifique [CNRS]
Traitement de l'Information Pour Images et Communications [TIPIC-SAMOVAR]
Communications, Images et Traitement de l'Information [TSP - CITI]
Septier, Francois [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Ecole nationale supérieure Mines-Télécom Lille Douai [IMT Lille Douai]
Salman, Naveed [Auteur]
University of Sheffield [Sheffield]
Mihaylova, Lyudmila [Auteur]
University of Sheffield [Sheffield]

Journal title :

IEEE Transactions on Signal and Information Processing over Networks

Pages :

510-518

Publisher :

IEEE

Publication date :

2018-09

ISSN :

2373-776X

English keyword(s) :

Gradient-based likelihood proposal
Monte Carlo methods
Shadow mapping
Sequential Markov chain Monte Carlo
Multiple target tracking
Correlated shadowing
Target tracking

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

Measurements in Wireless Sensor Networks (WSNs) are often correlated both in space and in time. This paper focuses on tracking multiple targets in WSNs by taking into consideration these measurement correlations. A Sequential ...
Show more >Measurements in Wireless Sensor Networks (WSNs) are often correlated both in space and in time. This paper focuses on tracking multiple targets in WSNs by taking into consideration these measurement correlations. A Sequential Markov Chain Monte Carlo (SMCMC) approach is proposed in which a Metropolis within Gibbs refinement step and a likelihood gradient proposal are introduced. This SMCMC filter is applied to case studies with cellular network Received Signal Strength (RSS) data in which the shadowing component correlations in space and time are estimated. The efficiency of the SMCMC approach compared to particle filtering, as well as the gradient proposal compared to a basic prior proposal, are demonstrated through numerical simulations. The accuracy improvement with the gradient-based SMCMC is above 90% when using a low number of particles. Thanks to its sequential nature, the proposed approach can be applied to various WSN applications, including traffic mobility monitoring and predictionShow less >

Language :

Anglais

Popular science :

Non

Collections :

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

Source :

Harvested from HAL