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

Michele Pace (1) , Pierre del Moral (2, 3)

1 IRIDA
2 ALEA - Advanced Learning Evolutionary Algorithms
3 IMB - Institut de Mathématiques de Bordeaux

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.

Domaines: Bibliothèque électronique [cs.DL]

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⟩