An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition

Xia, Yuxuan, Granstrom, Karl, Svensson, Lennart and Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 (2018) An Implementation of the Poisson Multi-Bernoulli Mixture Trajectory Filter via Dual Decomposition. In: 2018 21st International Conference on Information Fusion (FUSION 2018), 2018-7-10 - 2018-7-13.

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Abstract

This paper proposes an efficient implementation of the Poisson multi-Bernoulli mixture (PMBM) trajectory filter. The proposed implementation performs track-oriented N-scan pruning to limit complexity, and uses dual decomposition to solve the involved multi-frame assignment problem. In contrast to the existing PMBM filter for sets of targets, the PMBM trajectory filter is based on sets of trajectories which ensures that track continuity is formally maintained. The resulting filter is an efficient and scalable approximation to a Bayes optimal multi-target tracking algorithm, and its performance is compared, in a simulation study, to the PMBM target filter, and the delta generalized labelled multi-Bernoulli filter, in terms of state/trajectory estimation error and computational time.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	Bayesian estimation, multiple target tracking, random finite sets, set of trajectories, data association, multi-frame assignment
Depositing User:	Symplectic Admin
Date Deposited:	19 Feb 2019 10:21
Last Modified:	19 Jan 2023 01:02
DOI:	10.23919/icif.2018.8455236
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3033093