Title :

Sequential Markov Chain Monte Carlo for multi-target tracking with correlated RSS measurements

Author(s) :

Lamberti, Roland [Auteur]
Septier, Francois [Auteur]
LAGIS-SI
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Salman, Naveed [Auteur]
University of Sheffield [Sheffield]
Mihaylova, Lyudmila [Auteur]
University of Sheffield [Sheffield]

Conference title :

IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP)

City :

Singapore

Country :

Singapour

Start date of the conference :

2015-04-07

Publication date :

2015-04-07

HAL domain(s) :

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

English abstract : [en]

In this paper, we present a Bayesian approach to accurately track multiple objects based on Received Signal Strength (RSS) measure- ments. This work shows that taking into account the spatial correla- tions of the observations ...
Show more >In this paper, we present a Bayesian approach to accurately track multiple objects based on Received Signal Strength (RSS) measure- ments. This work shows that taking into account the spatial correla- tions of the observations caused by the random shadowing effect can induce significant tracking performance improvements, especially in very noisy scenarios. Additionally, the superiority of the proposed Sequential Markov Chain Monte Carlo (SMCMC) method over the more common Sequential Importance Resampling (SIR) technique is empirically demonstrated through numerical simulations in which multiple targets have to be trackedShow 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