Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter

Xia, Yuxuan, Granstrom, Karl, Svensson, Lennart, Garcia-Fernandez, Angel ORCID: 0000-0002-6471-8455 and Williams, Jason (2019) Multi-Scan Implementation of the Trajectory Poisson Multi-Bernoulli Mixture Filter Journal of Advances in Information Fusion, 14 (2). pp. 213-235. ISSN 1557-6418, 1557-6418

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Abstract

The Poisson multi-Bernoulli mixture (PMBM) and the multi-Bernoulli mixture (MBM) are two multitarget distributions for which closed-form filtering recursions exist. The PMBM has a Poisson birth process, whereas the MBM has a multi-Bernoulli birth process. This paper considers a recently developed formulation of the multitarget tracking problem using a random finite set of trajectories, through which the track continuity is explicitly established. A multiscan trajectory PMBM filter and a multiscan trajectory MBM filter, with the ability to correct past data association decisions to improve current decisions, are presented. In addition, a multiscan trajectory MBM<inf>01</inf> filter, in which the existence probabilities of all Bernoulli components are either 0 or 1, is presented. This paper proposes an efficient implementation that performs track-oriented N-scan pruning to limit computational complexity, and uses dual decomposition to solve the involved multiframe assignment problem. The performance of the presented multitarget trackers, applied with an efficient fixed-lag smoothing method, is evaluated in a simulation study.

Item Type:	Article
Depositing User:	Symplectic Admin
Date Deposited:	22 Nov 2019 09:15
Last Modified:	06 Jun 2025 18:50
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3063031
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