Fontana, Marco 
ORCID: 0000-0003-0703-6535, Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 and Maskell, Simon ORCID: 0000-0003-1917-2913
(2023)
Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter.
IEEE Transactions on Aerospace and Electronic Systems, 59 (5).
pp. 1-14.
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Abstract
This article proposes a clustering and merging approach for the Poisson multi-Bernoulli mixture (PMBM) filter to lower its computational complexity and make it suitable for multiple target tracking with a high number of targets. We define a measurement-driven clustering algorithm to reduce the data association problem into several subproblems, and we provide the derivation of the resulting clustered PMBM posterior density via Kullback-Leibler divergence minimization. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and intertrack swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than 1000 targets.
| Item Type: | Article |
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| Additional Information: | (c) 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. |
| Divisions: | Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 14 Mar 2023 10:18 |
| Last Modified: | 15 Mar 2024 08:52 |
| DOI: | 10.1109/taes.2023.3253662 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3169002 |
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