Multi-target PHD filtering: proposition of extensions to the multi-sensor case

Emmanuel Delande; Emmanuel Duflos; Dominique Heurguier; Philippe Vanheeghe

inria-00501502, version 3

Multi-target PHD filtering: proposition of extensions to the multi-sensor case

Emmanuel Delande ()^a^, 1, 2, Emmanuel Duflos ()^a^, 1, 2, Dominique Heurguier ³, Philippe Vanheeghe ()^a^, 1, 2

N° RR-7337 (2010)

Résumé : Common difficulties in multi-target tracking arise from the fact that the system state and the collection of measures are unordered and their size evolve randomly through time. The random finite set theory provides a powerful framework to cope with these issues. This document focuses more particularly on the PHD (Probability Hypothesis Density) filter proposed by Mahler. The first part of this report is a synthesis of Mahler's work and aims at providing a thorough description of the construction of the single-sensor PHD filter. Then, based on a few leads provided by Mahler, the second part proposes several extensions of this filter to the multi-sensor case.

a – Ecole Centrale de Lille
1 : Laboratoire d'Automatique, Génie Informatique et Signal (LAGIS)
CNRS : UMR8146 – Université Lille I - Sciences et technologies – Ecole Centrale de Lille
2 : SEQUEL (INRIA Lille - Nord Europe)
INRIA – CNRS : UMR8146 – Université Lille I - Sciences et technologies – Université Lille III - Sciences humaines et sociales – Ecole Centrale de Lille
3 : TCS - Thales Communication & Securité
Thales Communication & Securité (TCS) 92704 Colombes, France OPS / HAT / SPM

Collaboration : Thales Communications

inria-00501502, version 3
http://hal.inria.fr/inria-00501502
oai:hal.inria.fr:inria-00501502
Contributeur : Emmanuel Duflos <>
Soumis le : Mercredi 5 Janvier 2011, 16:29:05
Dernière modification le : Jeudi 9 Février 2012, 12:52:25

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%F inria-00501502, version 3
%0 Report
%U http://hal.inria.fr/inria-00501502
%2 INFO:INFO_TS;STAT:AP;SPI:SIGNAL
%T Multi-target PHD filtering: proposition of extensions to the multi-sensor case
%A Delande, Emmanuel
%A Duflos, Emmanuel
%A Heurguier, Dominique
%A Vanheeghe, Philippe
%+ Laboratoire d'Automatique, Génie Informatique et Signal - LAGIS , SEQUEL - INRIA Lille - Nord Europe , TCS - Thales Communication & Securité
%Z LAGIS-SI
%X Common difficulties in multi-target tracking arise from the fact that the system state and the collection of measures are unordered and their size evolve randomly through time. The random finite set theory provides a powerful framework to cope with these issues. This document focuses more particularly on the PHD (Probability Hypothesis Density) filter proposed by Mahler. The first part of this report is a synthesis of Mahler's work and aims at providing a thorough description of the construction of the single-sensor PHD filter. Then, based on a few leads provided by Mahler, the second part proposes several extensions of this filter to the multi-sensor case.
%2 Le pistage multi-cible se trouve confronté au double problème suivant : l'état du système et la collection de mesures ne sont pas ordonnés et leurs dimensions varient aléatoirement au cours du temps. Dans ce contexte, l'utilisation des ensembles aléatoires finis apporte un cadre de résolution particulièrement pertinent et ce travail s'intéresse plus particulièrement au filtre PHD (Probability Hypothesis Density) introduit par Mahler. La première partie de ce rapport est une synthèse des travaux de Mahler et se veut pédagogique : elle reprend en détail la construction du filtre PHD mono-capteur. En se basant sur les éléments de solution proposés par Mahler, la deuxième partie propose des extensions du filtre au cas multi-capteur.
%2 G.: Mathematics of Computing/G.3: PROBABILITY AND STATISTICS/G.3.7: Probabilistic algorithms (including Monte Carlo), G.3.14: Statistical software
%G Anglais
%2 Rapport de recherche
%P 64
%8 2010-07-20
%K PHD; Finite Sets Statistics,Multisensor Multitarget Tracking,
%2 2010-07-20
%Z RR-7337
%Z Bourse DGA - CNRS
%Z Thales Communications

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