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Multi-object filtering for pairwise Markov chains

Abstract : The Probability Hypothesis Density (PHD) Filter is a recent solution to the multi-target filtering problem which consists in estimating an unknown number of targets and their states. The PHD filter equations are derived under the assumption that the dynamics of the targets and associated observations follow a Hidden Markov Chain (HMC) model. HMC models have been recently extended to Pairwise Markov Chains (PMC) models. In this paper, we focus on multi-target filtering when targets and associated measurements follow a PMC model, and we extend the classical PHD filter to such models. We also propose a Gaussian Mixture (GM) implementation of our PMC PHD filter for linear and Gaussian PMC. Our approach enables to extend multi-object filtering to more general tracking scenarios, and also enables to deduce an estimate of the measurement associated to each target.
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https://hal.archives-ouvertes.fr/hal-00765497
Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Friday, December 14, 2012 - 4:41:49 PM
Last modification on : Saturday, September 26, 2020 - 3:25:35 AM

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Yohan Petetin, François Desbouvries. Multi-object filtering for pairwise Markov chains. ISSPA '12 : The 11th International Conference on Information Sciences, Signal Processing and their Applications, Jul 2012, Montreal, Canada. pp.348 -353, ⟨10.1109/ISSPA.2012.6310573⟩. ⟨hal-00765497⟩

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