A Particle Marginal Metropolis-Hastings Multi-Target Tracker
We propose a Bayesian multi-target batch processing algorithm capable of tracking an unknown number of targets that move close and/or cross each other in a dense clutter environment. The optimal Bayes multitarget tracking problem is formulated in the random finite set framework and a particle margin...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/34528 |
| _version_ | 1848754247219281920 |
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| author | Vu, T. Vo, Ba-Ngu Evans, R. |
| author_facet | Vu, T. Vo, Ba-Ngu Evans, R. |
| author_sort | Vu, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | We propose a Bayesian multi-target batch processing algorithm capable of tracking an unknown number of targets that move close and/or cross each other in a dense clutter environment. The optimal Bayes multitarget tracking problem is formulated in the random finite set framework and a particle marginal Metropolis-Hastings (PMMH) technique which is a combination of the Metropolis-Hastings (MH) algorithm and sequential Monte Carlo methods is applied to compute the multi-target posterior distribution. The PMMH technique is used to design a high-dimensional proposal distributions for the MH algorithm and allows the proposed batch process multi-target tracker to handle a large number of tracks in a computationally feasible manner. Our simulations show that the proposed tracker reliably estimates the number of tracks and their trajectories in scenarios with a large number of closely spaced tracks in a dense clutter environment albeit, more expensive than online methods. |
| first_indexed | 2025-11-14T08:37:22Z |
| format | Journal Article |
| id | curtin-20.500.11937-34528 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:37:22Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-345282017-09-13T15:11:57Z A Particle Marginal Metropolis-Hastings Multi-Target Tracker Vu, T. Vo, Ba-Ngu Evans, R. We propose a Bayesian multi-target batch processing algorithm capable of tracking an unknown number of targets that move close and/or cross each other in a dense clutter environment. The optimal Bayes multitarget tracking problem is formulated in the random finite set framework and a particle marginal Metropolis-Hastings (PMMH) technique which is a combination of the Metropolis-Hastings (MH) algorithm and sequential Monte Carlo methods is applied to compute the multi-target posterior distribution. The PMMH technique is used to design a high-dimensional proposal distributions for the MH algorithm and allows the proposed batch process multi-target tracker to handle a large number of tracks in a computationally feasible manner. Our simulations show that the proposed tracker reliably estimates the number of tracks and their trajectories in scenarios with a large number of closely spaced tracks in a dense clutter environment albeit, more expensive than online methods. 2014 Journal Article http://hdl.handle.net/20.500.11937/34528 10.1109/TSP.2014.2329270 IEEE restricted |
| spellingShingle | Vu, T. Vo, Ba-Ngu Evans, R. A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title | A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title_full | A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title_fullStr | A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title_full_unstemmed | A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title_short | A Particle Marginal Metropolis-Hastings Multi-Target Tracker |
| title_sort | particle marginal metropolis-hastings multi-target tracker |
| url | http://hdl.handle.net/20.500.11937/34528 |