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

Roland Lamberti 1 François Septier 2, 3, 4 Naveed Salman 5 Lyudmila Mihaylova 5
1 CITI - Communications, Images et Traitement de l'Information
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
3 Institut TELECOM/TELECOM Lille1
4 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
5 University of Sheffield [Sheffield]
Abstract : 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 tracked
Document type :
Conference papers
Complete list of metadatas
https://hal-imt.archives-ouvertes.fr/hal-01144848
Contributor : François Septier <>
Submitted on : Wednesday, April 22, 2015 - 7:18:54 PM
Last modification on : Monday, June 17, 2019 - 5:02:03 PM

Links full text

openaccess

Identifiers

Collections

INSTITUT-TELECOM | LAGIS | LAGIS-SI | TELECOM-SUDPARIS | CRISTAL-SIGMA | UNIV-PARIS-SACLAY | IMT-LILLE-DOUAI | CRISTAL | UNIV-LILLE

Citation

Roland Lamberti, François Septier, Naveed Salman, Lyudmila Mihaylova. Sequential Markov Chain Monte Carlo for multi-target tracking with correlated RSS measurements. IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Apr 2015, Singapore, Singapore. ⟨10.1109/ISSNIP.2015.7106901⟩. ⟨hal-01144848⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

244

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD