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Article Dans Une Revue IEEE Transactions on Aerospace and Electronic Systems Année : 2014

Marginalized particle PHD filters for multiple object Bayesian filtering

Résumé

The Probability Hypothesis Density (PHD) filter is a recent solution to the multi-target filtering problem. Because the PHD filter is not computable, several implementations have been proposed including the Gaussian Mixture (GM) approximations and Sequential Monte Carlo (SMC) methods. In this paper, we propose a marginalized particle PHD filter which improves the classical solutions when used in stochastic systems with partially linear substructure

Dates et versions

hal-01264791 , version 1 (29-01-2016)

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Yohan Petetin, Mark Morelande, François Desbouvries. Marginalized particle PHD filters for multiple object Bayesian filtering. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50 (2), pp.1182 - 1196 ⟨10.1109/TAES.2014.120805⟩. ⟨hal-01264791⟩
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