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

Emmanuel Delande 1, 2 Emmanuel Duflos 1, 2 Dominique Heurguier 3 Philippe Vanheeghe 1, 2
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, Inria Lille - Nord Europe, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
Abstract : 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.
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https://hal.inria.fr/inria-00501502
Contributor : Emmanuel Duflos <>
Submitted on : Wednesday, January 5, 2011 - 4:29:05 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Long-term archiving on : Friday, December 2, 2016 - 12:50:27 PM

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Emmanuel Delande, Emmanuel Duflos, Dominique Heurguier, Philippe Vanheeghe. Multi-target PHD filtering: proposition of extensions to the multi-sensor case. [Research Report] RR-7337, INRIA. 2010, pp.64. ⟨inria-00501502v3⟩

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