Trajectory Poisson Multi-Bernoulli Filters
Journal article, 2020

This paper presents two trajectory Poisson multi-Bernoulli (TPMB) filters for multi-target tracking: one to estimate the set of alive trajectories at each time step and another to estimate the set of all trajectories, which includes alive and dead trajectories, at each time step. The filters are based on propagating a Poisson multi-Bernoulli (PMB) density on the corresponding set of trajectories through the filtering recursion. After the update step, the posterior is a PMB mixture (PMBM) so, in order to obtain a PMB density, a Kullback-Leibler divergence minimisation on an augmented space is performed. The developed filters are computationally lighter alternatives to the trajectory PMBM filters, which provide the closed-form recursion for sets of trajectories with Poisson birth model, and are shown to outperform previous multi-target tracking algorithms.

sets of trajectories

Multitarget tracking

poisson multi-bernoulli filter


Angel Garcia

University of Liverpool

Lennart Svensson

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Signal Processing

Jason L. Williams

University of Liverpool

Yuxuan Xia

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Karl Granström

University of Liverpool

IEEE Transactions on Signal Processing

1053-587X (ISSN)

Vol. 68 4933-4945 9169859

Subject Categories

Probability Theory and Statistics

Control Engineering

Signal Processing



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