A labeled random finite set spawning model
© 2017 IEEE. Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths...
| Main Authors: | , , , |
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| Format: | Conference Paper |
| Published: |
2017
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| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/68138 |
| _version_ | 1848761753139150848 |
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| author | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. |
| author_facet | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. |
| author_sort | Bryant, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 IEEE. Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage. |
| first_indexed | 2025-11-14T10:36:41Z |
| format | Conference Paper |
| id | curtin-20.500.11937-68138 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:36:41Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-681382022-10-27T07:12:36Z A labeled random finite set spawning model Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. © 2017 IEEE. Previous labeled random finite set filter developments use a target motion model that only accounts for survival and birth. While such a model provides the means for a multi-target tracking filter such as the Generalized Labeled Multi-Bernoulli filter to capture target births and deaths in a wide variety of applications, it lacks the capability to capture the lineages of spawned target tracks. In this paper, we propose a labeled random finite set spawning model and derive the resulting multi-target prediction and filtering densities. This formulation enables the joint estimation of spawned object's state and and information regarding its lineage. 2017 Conference Paper http://hdl.handle.net/20.500.11937/68138 10.1109/ICCAIS.2017.8217579 http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP160104662 restricted |
| spellingShingle | Bryant, D. Vo, Ba Tuong Vo, Ba-Ngu Jones, B. A labeled random finite set spawning model |
| title | A labeled random finite set spawning model |
| title_full | A labeled random finite set spawning model |
| title_fullStr | A labeled random finite set spawning model |
| title_full_unstemmed | A labeled random finite set spawning model |
| title_short | A labeled random finite set spawning model |
| title_sort | labeled random finite set spawning model |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/68138 |