Trajectory probability hypothesis density filter
Paper in proceeding, 2018

This paper presents the probability hypothesis density (PHD) filter for sets of trajectories: the trajectory probability density (TPHD) filter. The TPHD filter is capable of estimating trajectories in a principled way without requiring to evaluate all measurement-to-target association hypotheses. The TPHD filter is based on recursively obtaining the best Poisson approximation to the multitrajectory filtering density in the sense of minimising the Kullback-Leibler divergence. We also propose a Gaussian mixture implementation of the TPHD recursion. Finally, we include simulation results to show the performance of the proposed algorithm.

multitarget tracking

PHD filter

Random finite sets

sets of trajectories


Angel Garcia

University of Liverpool

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering

2018 21st International Conference on Information Fusion, FUSION 2018

1430-1437 8455270
978-0-9964527-6-2 (ISBN)

21st International Conference on Information Fusion, FUSION 2018
Cambridge, United Kingdom,

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing



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