Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation

Xia, Yuxuan ORCID: 0000-0002-2788-7911, García-Fernández, Ángel F ORCID: 0000-0002-6471-8455, Meyer, Florian ORCID: 0000-0001-6985-2250, Williams, Jason L ORCID: 0000-0002-2416-075X, Granström, Karl ORCID: 0000-0002-3450-988X and Svensson, Lennart ORCID: 0000-0003-0206-9186 (2023) Trajectory PMB Filters for Extended Object Tracking Using Belief Propagation. IEEE Transactions on Aerospace and Electronic Systems, 59 (6). pp. 9312-9331.

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Official URL: http://dx.doi.org/10.1109/taes.2023.3317233

Abstract

In this article, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior after the update step is approximated as a PMB. The efficient PMB approximation relies on several important theoretical contributions. First, we present a PMBM conjugate prior on the posterior of sets of trajectories for a generalized measurement model, in which each object generates an independent set of measurements. The PMBM density is a conjugate prior in the sense that both the prediction and the update steps preserve the PMBM form of the density. Second, we present a factor graph representation of the joint posterior of the PMBM set of trajectories and association variables for the Poisson spatial measurement model. Importantly, leveraging the PMBM conjugacy and the factor graph formulation enables an elegant treatment on undetected objects via a Poisson point process and efficient inference on sets of trajectories using BP, where the approximate marginal densities in the PMB approximation can be obtained without enumeration of different data association hypotheses. To achieve this, we present a particle-based implementation of the proposed filter, where smoothed trajectory estimates, if desired, can be obtained via single-object particle smoothing methods, and its performance for EOT with ellipsoidal shapes is evaluated in a simulation study.

Item Type:	Article
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	20 Sep 2023 08:25
Last Modified:	17 Mar 2024 18:26
DOI:	10.1109/taes.2023.3317233
Related URLs:	Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172893