Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering

Xia, Yuxuan, Granstrom, Karl, Svensson, Lennart, Fatemi, Maryam, Garcia-Fernandez, Angel ORCID: 0000-0002-6471-8455 and Williams, Jason (2021) Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering. IEEE Transactions on Aerospace and Electronic Systems, 58 (2). pp. 890-906.

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Official URL: http://dx.doi.org/10.1109/taes.2021.3111720

Abstract

The Poisson multi-Bernoulli mixture (PMBM) is a multiobject conjugate prior for the closed-form Bayes random finite set filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This article considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented, where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback Leibler divergence minimization. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study.

Item Type:	Article
Uncontrolled Keywords:	Density measurement, Computational modeling, Time measurement, Clutter, Standards, Bayes methods, Radar tracking, Extended object, Gaussian inverse Wishart, Kullback-Leibler divergence (KLD), multi-Bernoulli (MB), multiobject conjugate prior, multiobject filtering, random finite sets (RFSs)
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	13 Sep 2021 07:39
Last Modified:	17 Mar 2024 12:44
DOI:	10.1109/TAES.2021.3111720
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3136837