CPHD Filtering With Unknown Clutter Rate and Detection Profile
In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinaliz...
| Main Authors: | , , |
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| Format: | Journal Article |
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Institute of Electrical and Electronics Engineers
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/6872 |
| _version_ | 1848745202350555136 |
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| author | Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Mahler, R. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions. |
| first_indexed | 2025-11-14T06:13:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-6872 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:13:36Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-68722017-09-13T14:35:42Z CPHD Filtering With Unknown Clutter Rate and Detection Profile Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu PHD Finite set statistics parameter estimation robust filtering CPHD multi-target tracking In Bayesian multi-target filtering, we have to contend with two notable sources of uncertainty, clutter and detection. Knowledge of parameters such as clutter rate and detection profile are of critical importance in multi-target filters such as the probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. Significant mismatches in clutter and detection model parameters result in biased estimates. In practice, these model parameters are often manually tuned or estimated offline from training data. In this paper we propose PHD/CPHD filters that can accommodate model mismatch in clutter rate and detection profile. In particular we devise versions of the PHD/CPHD filters that can adaptively learn the clutter rate and detection profile while filtering. Moreover, closed-form solutions to these filtering recursions are derived using Beta and Gaussian mixtures. Simulations are presented to verify the proposed solutions. 2011 Journal Article http://hdl.handle.net/20.500.11937/6872 10.1109/TSP.2011.2128316 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | PHD Finite set statistics parameter estimation robust filtering CPHD multi-target tracking Mahler, R. Vo, Ba Tuong Vo, Ba-Ngu CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title | CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title_full | CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title_fullStr | CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title_full_unstemmed | CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title_short | CPHD Filtering With Unknown Clutter Rate and Detection Profile |
| title_sort | cphd filtering with unknown clutter rate and detection profile |
| topic | PHD Finite set statistics parameter estimation robust filtering CPHD multi-target tracking |
| url | http://hdl.handle.net/20.500.11937/6872 |