Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking
In this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two “present” or “absent” modes. Finite set statistics i...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Institute of Electrical and Electronics Engineers
2011
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/33662 |
| _version_ | 1848754009589940224 |
|---|---|
| author | Vo, Ba Tuong Clark, D. Vo, Ba-Ngu Ristic, B. |
| author_facet | Vo, Ba Tuong Clark, D. Vo, Ba-Ngu Ristic, B. |
| author_sort | Vo, Ba Tuong |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | In this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two “present” or “absent” modes. Finite set statistics is used to derive the smoothing recursion. Our results indicate that smoothing has two distinct advantages over just using filtering: First, we are able to more accurately identify the appearance and disappearance of a target in the scene, and second, we can provide improved state estimates when the target exists. |
| first_indexed | 2025-11-14T08:33:36Z |
| format | Journal Article |
| id | curtin-20.500.11937-33662 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:33:36Z |
| publishDate | 2011 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-336622017-09-13T15:32:25Z Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking Vo, Ba Tuong Clark, D. Vo, Ba-Ngu Ristic, B. tracking filtering estimation Detection smoothing In this correspondence, we derive a forward-backward smoother for joint target detection and estimation and propose a sequential Monte Carlo implementation. We model the target by a Bernoulli random finite set since the target can be in one of two “present” or “absent” modes. Finite set statistics is used to derive the smoothing recursion. Our results indicate that smoothing has two distinct advantages over just using filtering: First, we are able to more accurately identify the appearance and disappearance of a target in the scene, and second, we can provide improved state estimates when the target exists. 2011 Journal Article http://hdl.handle.net/20.500.11937/33662 10.1109/TSP.2011.2158427 Institute of Electrical and Electronics Engineers restricted |
| spellingShingle | tracking filtering estimation Detection smoothing Vo, Ba Tuong Clark, D. Vo, Ba-Ngu Ristic, B. Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title | Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title_full | Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title_fullStr | Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title_full_unstemmed | Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title_short | Bernoulli Forward-Backward Smoothing for Joint Target Detection and Tracking |
| title_sort | bernoulli forward-backward smoothing for joint target detection and tracking |
| topic | tracking filtering estimation Detection smoothing |
| url | http://hdl.handle.net/20.500.11937/33662 |