Mean-Field PHD Filters Based on Generalized Feynman-Kac Flow

Michele Pace 1 Pierre Del Moral 2, 3
1 IRIDA
IRIDIA - Institut de Recherches interdisciplinaires et de Développements en Intelligence Artificielle [Bruxelles]
2 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : We discuss a connection between spatial branching processes and the PHD recursion based on conditioning principles for Poisson Point Processes. The branching process formulation gives a generalized Feynman-Kac systems interpretation of the PHD filtering equations, which enables the derivation of mean-field implementations of the PHD filter. This approach provides a principled means for obtaining target tracks and alleviates the need for pruning, merging and clustering for the estimation of multi-target states.
Type de document :
Article dans une revue
IEEE Journal of Selected Topics in Signal Processing, IEEE, 2013, 7 (3), 〈10.1109/JSTSP.2013.2250909〉
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https://hal.inria.fr/hal-00932284
Contributeur : Pierre Del Moral <>
Soumis le : jeudi 16 janvier 2014 - 16:02:17
Dernière modification le : jeudi 11 janvier 2018 - 06:22:36

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Michele Pace, Pierre Del Moral. Mean-Field PHD Filters Based on Generalized Feynman-Kac Flow. IEEE Journal of Selected Topics in Signal Processing, IEEE, 2013, 7 (3), 〈10.1109/JSTSP.2013.2250909〉. 〈hal-00932284〉

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