Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866916392108818432 |
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| author | Fontana, Marco García-Fernández, Ángel F. Maskell, Simon |
| author_facet | Fontana, Marco García-Fernández, Ángel F. Maskell, Simon |
| contents | This paper 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 minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2205_14021 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter Fontana, Marco García-Fernández, Ángel F. Maskell, Simon Signal Processing Computation This paper 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 minimisation. Furthermore, we investigate different strategies to reduce the number of single target hypotheses by approximating the posterior via merging and inter-track swapping of Bernoulli components. We evaluate the performance of the proposed algorithm on simulated tracking scenarios with more than one thousand targets. |
| title | Data-driven clustering and Bernoulli merging for the Poisson multi-Bernoulli mixture filter |
| topic | Signal Processing Computation |
| url | https://arxiv.org/abs/2205.14021 |