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

Michele Pace 1 Pierre del Moral 2, 3 
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.
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Submitted on : Thursday, January 16, 2014 - 4:02:17 PM
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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, 2013, 7 (3), ⟨10.1109/JSTSP.2013.2250909⟩. ⟨hal-00932284⟩



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