Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter
The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the prese...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
IEEE
2009
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| Online Access: | http://hdl.handle.net/20.500.11937/14735 |
| _version_ | 1848748702739464192 |
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| author | Panta, K. Clark, D. Vo, Ba-Ngu |
| author_facet | Panta, K. Clark, D. Vo, Ba-Ngu |
| author_sort | Panta, K. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own. |
| first_indexed | 2025-11-14T07:09:15Z |
| format | Journal Article |
| id | curtin-20.500.11937-14735 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:09:15Z |
| publishDate | 2009 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-147352017-09-13T14:07:13Z Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter Panta, K. Clark, D. Vo, Ba-Ngu The Gaussian mixture probability hypothesis density (GM-PHD) recursion is a closed-form solution to the probability hypothesis density (PHD) recursion, which was proposed for jointly estimating the time-varying number of targets and their states from a sequence of noisy measurement sets in the presence of data association uncertainty, clutter, and miss-detection. However the GM-PHD filter does not provide identities of individual target state estimates, that are needed to construct tracks of individual targets. In this paper, we propose a new multi-target tracker based on the GM-PHD filter, which gives the association amongst state estimates of targets over time and provides track labels. Various issues regarding initiating, propagating and terminating tracks are discussed. Furthermore, we also propose a technique for resolving identities of targets in close proximity, which the PHD filter is unable to do on its own. 2009 Journal Article http://hdl.handle.net/20.500.11937/14735 10.1109/TAES.2009.5259179 IEEE fulltext |
| spellingShingle | Panta, K. Clark, D. Vo, Ba-Ngu Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title | Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title_full | Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title_fullStr | Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title_full_unstemmed | Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title_short | Data Association and Track Management for the Gaussian Mixture Probability Hypothesis Density Filter |
| title_sort | data association and track management for the gaussian mixture probability hypothesis density filter |
| url | http://hdl.handle.net/20.500.11937/14735 |