'Statistics 102' for multisource-multitarget detection and tracking
This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finit...
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| Format: | Journal Article |
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Institute of Electrical and Electronic Engineers
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/56146 |
| _version_ | 1848759798159376384 |
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| author | Mahler, Ronald |
| author_facet | Mahler, Ronald |
| author_sort | Mahler, Ronald |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finite-set statistics provides powerful new conceptual and computational methods for dealing with multisensor-multitarget detection and tracking problems. The paper describes how 'multitarget integro-differential calculus' is used to extend conventional single-sensor, single-target formal Bayesian motion and measurement modeling to general tracking problems. Given such models, the paper describes the Bayes-optimal approach to multisensor-multitarget detection and tracking: the multisensor-multitarget recursive Bayes filter. Finally, it describes how multitarget calculus is used to derive principled statistical approximations of this optimal filter, such as PHD filters, CPHD filters, and multi-Bernoulli filters. © 2007-2012 IEEE. |
| first_indexed | 2025-11-14T10:05:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-56146 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:36Z |
| publishDate | 2013 |
| publisher | Institute of Electrical and Electronic Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-561462017-09-13T16:11:24Z 'Statistics 102' for multisource-multitarget detection and tracking Mahler, Ronald This tutorial paper summarizes the motivations, concepts and techniques of finite-set statistics (FISST), a system-level, 'top-down,' direct generalization of ordinary single-sensor, single-target engineering statistics to the realm of multisensor, multitarget detection and tracking. Finite-set statistics provides powerful new conceptual and computational methods for dealing with multisensor-multitarget detection and tracking problems. The paper describes how 'multitarget integro-differential calculus' is used to extend conventional single-sensor, single-target formal Bayesian motion and measurement modeling to general tracking problems. Given such models, the paper describes the Bayes-optimal approach to multisensor-multitarget detection and tracking: the multisensor-multitarget recursive Bayes filter. Finally, it describes how multitarget calculus is used to derive principled statistical approximations of this optimal filter, such as PHD filters, CPHD filters, and multi-Bernoulli filters. © 2007-2012 IEEE. 2013 Journal Article http://hdl.handle.net/20.500.11937/56146 10.1109/JSTSP.2013.2253084 Institute of Electrical and Electronic Engineers restricted |
| spellingShingle | Mahler, Ronald 'Statistics 102' for multisource-multitarget detection and tracking |
| title | 'Statistics 102' for multisource-multitarget detection and tracking |
| title_full | 'Statistics 102' for multisource-multitarget detection and tracking |
| title_fullStr | 'Statistics 102' for multisource-multitarget detection and tracking |
| title_full_unstemmed | 'Statistics 102' for multisource-multitarget detection and tracking |
| title_short | 'Statistics 102' for multisource-multitarget detection and tracking |
| title_sort | 'statistics 102' for multisource-multitarget detection and tracking |
| url | http://hdl.handle.net/20.500.11937/56146 |