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
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Communication dans un congrès
IEEE 10th International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Apr 2015, Singapore, Singapore. 2015, 〈10.1109/ISSNIP.2015.7106901〉
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https://hal-imt.archives-ouvertes.fr/hal-01144848
Contributeur : François Septier <>
Soumis le : mercredi 22 avril 2015 - 19:18:54
Dernière modification le : mardi 3 juillet 2018 - 11:49:17

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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. 2015, 〈10.1109/ISSNIP.2015.7106901〉. 〈hal-01144848〉

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