A mixed GM/SMC implementation of the probability hypothesis density filter - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès ISSPA '12 : The 11th International Conference on Information Sciences, Signal Processing and their Applications Année : 2012

A mixed GM/SMC implementation of the probability hypothesis density filter

Résumé

The Probability Hypothesis Density (PHD) filter is a recent solution for tracking an unknown number of targets in a multi-object environment. The PHD filter cannot be computed exactly, but popular implementations include Gaussian Mixture (GM) and Sequential Monte Carlo (SMC) based algorithms. GM implementations suffer from pruning and merging approximations, but enable to extract the states easily; on the other hand, SMC implementations are of interest if the discrete approximation is relevant, but are penalized by the difficulty to guide particles towards promising regions and to extract the states. In this paper, we propose a mixed GM/SMC implementation of the PHD filter which does not suffer from the above mentioned drawbacks. Due to the SMC part, our algorithm can be used in models where the GM implementation is unavailable; but it also benefits from the easy state extraction of GM techniques, without requiring pruning or merging approximations. Our algorithm is validated on simulations.
Fichier non déposé

Dates et versions

hal-00765490 , version 1 (14-12-2012)

Identifiants

Citer

Yohan Petetin, François Desbouvries. A mixed GM/SMC implementation of the probability hypothesis density filter. ISSPA '12 : The 11th International Conference on Information Sciences, Signal Processing and their Applications, Jul 2012, Montreal, Canada. pp.425-430, ⟨10.1109/ISSPA.2012.6310588⟩. ⟨hal-00765490⟩
95 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More