Bayesian Sequential Track Formation
This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current m...
| Main Authors: | , , |
|---|---|
| Format: | Journal Article |
| Published: |
IEEE
2014
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/21419 |
| _version_ | 1848750585221742592 |
|---|---|
| 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 presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square labeled optimal subpatternassignment error. This method requires knowledge of the posterior density of the vector-valued state. The second assigns the labeling that maximizes the probability that the current multi-targetstate estimate is optimally linked with the available tracks at the previous time step. In this case, we only require knowledge of the random finite-set posterior density without labels. |
| first_indexed | 2025-11-14T07:39:10Z |
| format | Journal Article |
| id | curtin-20.500.11937-21419 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:39:10Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-214192017-09-13T13:55:43Z Bayesian Sequential Track Formation Garcia Fernandez, Angel Morelande, M. Grajal, J. Target labelling Bayesian framework multiple target tracking random finite sets This paper presents a theoretical framework for track building in multiple-target scenarios from the Bayesian point of view. It is assumed that the number of targets is fixed and known. We propose two optimal methods for building tracks sequentially. The first one uses the labelling of the current multitarget state estimate that minimizes the mean-square labeled optimal subpatternassignment error. This method requires knowledge of the posterior density of the vector-valued state. The second assigns the labeling that maximizes the probability that the current multi-targetstate estimate is optimally linked with the available tracks at the previous time step. In this case, we only require knowledge of the random finite-set posterior density without labels. 2014 Journal Article http://hdl.handle.net/20.500.11937/21419 10.1109/TSP.2014.2364013 IEEE fulltext |
| spellingShingle | Target labelling Bayesian framework multiple target tracking random finite sets Garcia Fernandez, Angel Morelande, M. Grajal, J. Bayesian Sequential Track Formation |
| title | Bayesian Sequential Track Formation |
| title_full | Bayesian Sequential Track Formation |
| title_fullStr | Bayesian Sequential Track Formation |
| title_full_unstemmed | Bayesian Sequential Track Formation |
| title_short | Bayesian Sequential Track Formation |
| title_sort | bayesian sequential track formation |
| topic | Target labelling Bayesian framework multiple target tracking random finite sets |
| url | http://hdl.handle.net/20.500.11937/21419 |