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 TSP - CITI - Communications, Images et Traitement de l'Information
2 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
3 Institut TELECOM/TELECOM Lille1
4 LAGIS-SI
5 University of Sheffield [Sheffield]

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

Domaines: Traitement du signal et de l'image [eess.SP]

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⟩