Mean-Field PHD Filters Based on Generalized Feynman-Kac Flow
DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
DEL MORAL, Pierre
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
< Reduce
Advanced Learning Evolutionary Algorithms [ALEA]
Institut de Mathématiques de Bordeaux [IMB]
Language
en
Article de revue
This item was published in
IEEE Journal of Selected Topics in Signal Processing. 2013, vol. 7, n° 3
IEEE
English 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 ...Read more >
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.Read less <
Origin
Hal imported