Particle filter for extracting target label information when targets move in close proximity
This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multim...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2011
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| Online Access: | http://hdl.handle.net/20.500.11937/53912 |
| _version_ | 1848759257400344576 |
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| author | Garcia Fernandez, Angel Morelande, M. Grajal, J. |
| author_facet | Garcia Fernandez, Angel Morelande, M. Grajal, J. |
| author_sort | Garcia Fernandez, Angel |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filer, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators. © 2011 IEEE. |
| first_indexed | 2025-11-14T09:57:00Z |
| format | Conference Paper |
| id | curtin-20.500.11937-53912 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:57:00Z |
| publishDate | 2011 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-539122017-06-23T03:01:51Z Particle filter for extracting target label information when targets move in close proximity Garcia Fernandez, Angel Morelande, M. Grajal, J. This paper addresses the problem of approximating the posterior probability density function of two targets after a crossing from the Bayesian perspective such that the information about target labels is not lost. To this end, we develop a particle filter that is able to maintain the inherent multimodality of the posterior after the targets have moved in close proximity. Having this approximation available, we are able to extract information about target labels even when the measurements do not provide information about target's identities. In addition, due to the structure of our particle filer, we are able to use an estimator that provides lower optimal subpattern assignment (OSPA) errors than usual estimators. © 2011 IEEE. 2011 Conference Paper http://hdl.handle.net/20.500.11937/53912 restricted |
| spellingShingle | Garcia Fernandez, Angel Morelande, M. Grajal, J. Particle filter for extracting target label information when targets move in close proximity |
| title | Particle filter for extracting target label information when targets move in close proximity |
| title_full | Particle filter for extracting target label information when targets move in close proximity |
| title_fullStr | Particle filter for extracting target label information when targets move in close proximity |
| title_full_unstemmed | Particle filter for extracting target label information when targets move in close proximity |
| title_short | Particle filter for extracting target label information when targets move in close proximity |
| title_sort | particle filter for extracting target label information when targets move in close proximity |
| url | http://hdl.handle.net/20.500.11937/53912 |