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Sequential Markov Chain Monte Carlo for multi-target tracking with correlated RSS measurements
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
https://hal-imt.archives-ouvertes.fr/hal-01144848
Contributor : François Septier Connect in order to contact the contributor
Submitted on : Wednesday, April 22, 2015 - 7:18:54 PM
Last modification on : Monday, September 20, 2021 - 11:38:24 AM
Contributor : François Septier Connect in order to contact the contributor
Submitted on : Wednesday, April 22, 2015 - 7:18:54 PM
Last modification on : Monday, September 20, 2021 - 11:38:24 AM
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