Multi-target Track-Before-Detect using labeled random finite set
Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance....
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/26035 |
| _version_ | 1848751871692374016 |
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| author | Papi, F. Vo, Ba Tuong Bocquel, M. Vo, Ba-Ngu |
| author2 | N/A |
| author_facet | N/A Papi, F. Vo, Ba Tuong Bocquel, M. Vo, Ba-Ngu |
| author_sort | Papi, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multitarget TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach. |
| first_indexed | 2025-11-14T07:59:37Z |
| format | Conference Paper |
| id | curtin-20.500.11937-26035 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:59:37Z |
| publishDate | 2013 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-260352017-09-13T15:23:05Z Multi-target Track-Before-Detect using labeled random finite set Papi, F. Vo, Ba Tuong Bocquel, M. Vo, Ba-Ngu N/A Multi-target tracking requires the joint estimation of the number of target trajectories and their states from a sequence of observations. In low signal-to-noise ratio (SNR) scenarios, the poor detection probability and large number of false observations can greatly degrade the tracking performance. In this case an approach called Track-Before-Detect (TBD) that operates on the pre-detection signal, is needed. In this paper we present a labeled random finite set solution to the multitarget TBD problem. To the best of our knowledge this is the first provably Bayes optimal approach to multi-target tracking using image data. Simulation results using realistic radar-based TBD scenarios are also presented to demonstrate the capability of the proposed approach. 2013 Conference Paper http://hdl.handle.net/20.500.11937/26035 10.1109/ICCAIS.2013.6720540 IEEE restricted |
| spellingShingle | Papi, F. Vo, Ba Tuong Bocquel, M. Vo, Ba-Ngu Multi-target Track-Before-Detect using labeled random finite set |
| title | Multi-target Track-Before-Detect using labeled random finite set |
| title_full | Multi-target Track-Before-Detect using labeled random finite set |
| title_fullStr | Multi-target Track-Before-Detect using labeled random finite set |
| title_full_unstemmed | Multi-target Track-Before-Detect using labeled random finite set |
| title_short | Multi-target Track-Before-Detect using labeled random finite set |
| title_sort | multi-target track-before-detect using labeled random finite set |
| url | http://hdl.handle.net/20.500.11937/26035 |