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.
ISSN 0018-9251, 1557-9603
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EPMB_final.pdf - Author Accepted Manuscript Download (4MB) | Preview |
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 & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 13 Sep 2021 07:39 |
| Last Modified: | 01 Mar 2026 00:34 |
| DOI: | 10.1109/TAES.2021.3111720 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3136837 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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