Titre :

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

Auteur(s) :

Lamberti, Roland [Auteur]
Communications, Images et Traitement de l'Information [TSP - CITI]
Septier, Francois [Auteur]
LAGIS-SI
Institut TELECOM/TELECOM Lille1
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]

Titre de la manifestation scientifique :

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

Ville :

Singapore

Pays :

Singapour

Date de début de la manifestation scientifique :

2015-04-07

Date de publication :

2015-04-07

Discipline(s) HAL :

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

Résumé en anglais : [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 ...
Lire la suite >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 trackedLire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Collections :

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

Source :

Harvested from HAL