Bayesian multi-target tracking with merged measurements using labelled random finite sets
Most tracking algorithms in the literature assume that the targets always generate measurements independently of each other, i.e., the sensor is assumed to have infinite resolution. Such algorithms have been dominant because addressing the presence of merged measurements increases the computational...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/28547 |
| _version_ | 1848752566653943808 |
|---|---|
| author | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu |
| author_facet | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu |
| author_sort | Beard, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Most tracking algorithms in the literature assume that the targets always generate measurements independently of each other, i.e., the sensor is assumed to have infinite resolution. Such algorithms have been dominant because addressing the presence of merged measurements increases the computational complexity of the tracking problem, and limitations on computing resources often make this infeasible. When merging occurs, these algorithms suffer degraded performance, often due to tracks being terminated too early. In this paper, we use the theory of random finite sets (RFS) to develop a principled Bayesian solution to tracking an unknown and variable number of targets in the presence of merged measurements. We propose two tractable implementations of the resulting filter, with differing computational requirements. The performance of these algorithms is demonstrated by Monte Carlo simulations of a multi-target bearings-only scenario, where measurements become merged due to the effect of finite sensor resolution. |
| first_indexed | 2025-11-14T08:10:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-28547 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:10:40Z |
| publishDate | 2015 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-285472017-09-13T15:20:01Z Bayesian multi-target tracking with merged measurements using labelled random finite sets Beard, M. Vo, Ba Tuong Vo, Ba-Ngu Most tracking algorithms in the literature assume that the targets always generate measurements independently of each other, i.e., the sensor is assumed to have infinite resolution. Such algorithms have been dominant because addressing the presence of merged measurements increases the computational complexity of the tracking problem, and limitations on computing resources often make this infeasible. When merging occurs, these algorithms suffer degraded performance, often due to tracks being terminated too early. In this paper, we use the theory of random finite sets (RFS) to develop a principled Bayesian solution to tracking an unknown and variable number of targets in the presence of merged measurements. We propose two tractable implementations of the resulting filter, with differing computational requirements. The performance of these algorithms is demonstrated by Monte Carlo simulations of a multi-target bearings-only scenario, where measurements become merged due to the effect of finite sensor resolution. 2015 Journal Article http://hdl.handle.net/20.500.11937/28547 10.1109/TSP.2015.2393843 Institute of Electrical and Electronics Engineers Inc. restricted |
| spellingShingle | Beard, M. Vo, Ba Tuong Vo, Ba-Ngu Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title | Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title_full | Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title_fullStr | Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title_full_unstemmed | Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title_short | Bayesian multi-target tracking with merged measurements using labelled random finite sets |
| title_sort | bayesian multi-target tracking with merged measurements using labelled random finite sets |
| url | http://hdl.handle.net/20.500.11937/28547 |