Marginalized particle PHD filters for multiple object bayesian filtering

Yohan Petetin; Mark Morelande; Francois Desbouvries

doi:10.1109/TAES.2014.120805

Marginalized particle PHD filters for multiple object bayesian filtering

Institut Mines-Télécom
University of Melbourne

Résultats de recherche: Contribution à un journal › Article › Revue par des pairs

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.

langue originale	Anglais
Numéro d'article	6850148
Pages (de - à)	1182-1196
Nombre de pages	15
journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	50
Numéro de publication	2
Les DOIs	https://doi.org/10.1109/TAES.2014.120805
état	Publié - 1 janv. 2014

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10.1109/TAES.2014.120805

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abstract = "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.",

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Marginalized particle PHD filters for multiple object bayesian filtering

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