Tracking Multiple Spawning Targets Using Poisson Multi-Bernoulli Mixtures on Sets of Tree Trajectories

Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 and Svensson, Lennart (2022) Tracking Multiple Spawning Targets Using Poisson Multi-Bernoulli Mixtures on Sets of Tree Trajectories. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 70. pp. 1987-1999.

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Official URL: http://dx.doi.org/10.1109/TSP.2022.3165947

Abstract

This paper proposes a Poisson multi-Bernoulli mixture (PMBM) filter on the space of sets of tree trajectories for multiple target tracking with spawning targets. A tree trajectory contains all trajectory information of a target and its descendants, which appear due to the spawning process. Each tree contains a set of branches, where each branch has trajectory information of a target or one of the descendants and its genealogy. For the standard dynamic and measurement models with multi-Bernoulli spawning, the posterior is a PMBM density, with each Bernoulli having information on a potential tree trajectory. To enable a computationally efficient implementation, we derive an approximate PMBM filter in which each Bernoulli tree trajectory has multi-Bernoulli branches, obtained by minimising the Kullback-Leibler divergence. The resulting filter improves tracking performance of state-of-the-art algorithms in a simulated scenario.

Item Type:	Article
Additional Information:	Matlab code can be found at https://github.com/Agarciafernandez
Uncontrolled Keywords:	Trajectory, Information filters, Filtering algorithms, Time measurement, Target tracking, Density measurement, Standards, Multiple target tracking, spawning, Poisson multi-Bernoulli mixture, sets of tree trajectories
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	11 Apr 2022 13:43
Last Modified:	18 Jan 2023 21:05
DOI:	10.1109/TSP.2022.3165947
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3152675