The Labeled Multi-Bernoulli Filter
This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filte...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
IEEE
2014
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/4136 |
| _version_ | 1848744430589181952 |
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| author | Reuter, S. Vo, Ba Tuong Vo, Ba-Ngu Dietmayer, K. |
| author_facet | Reuter, S. Vo, Ba Tuong Vo, Ba-Ngu Dietmayer, K. |
| author_sort | Reuter, S. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the $delta$-Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the $delta$ -Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance. |
| first_indexed | 2025-11-14T06:01:20Z |
| format | Journal Article |
| id | curtin-20.500.11937-4136 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:01:20Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-41362017-09-13T14:33:02Z The Labeled Multi-Bernoulli Filter Reuter, S. Vo, Ba Tuong Vo, Ba-Ngu Dietmayer, K. marked point process conjugate prior random finite set target tracking Bayesian estimation This paper proposes a generalization of the multi- Bernoulli filter called the labeled multi-Bernoulli filter that outputs target tracks. Moreover, the labeled multi-Bernoulli filter does not exhibit a cardinality bias due to a more accurate update approximation compared to the multi-Bernoulli filter by exploiting the conjugate prior form for labeled Random Finite Sets. The proposed filter can be interpreted as an efficient approximation of the $delta$-Generalized Labeled Multi-Bernoulli filter. It inherits the advantages of the multi-Bernoulli filter in regards to particle implementation and state estimation. It also inherits advantages of the $delta$ -Generalized Labeled Multi-Bernoulli filter in that it outputs (labeled) target tracks and achieves better performance. 2014 Journal Article http://hdl.handle.net/20.500.11937/4136 10.1109/TSP.2014.2323064 IEEE restricted |
| spellingShingle | marked point process conjugate prior random finite set target tracking Bayesian estimation Reuter, S. Vo, Ba Tuong Vo, Ba-Ngu Dietmayer, K. The Labeled Multi-Bernoulli Filter |
| title | The Labeled Multi-Bernoulli Filter |
| title_full | The Labeled Multi-Bernoulli Filter |
| title_fullStr | The Labeled Multi-Bernoulli Filter |
| title_full_unstemmed | The Labeled Multi-Bernoulli Filter |
| title_short | The Labeled Multi-Bernoulli Filter |
| title_sort | labeled multi-bernoulli filter |
| topic | marked point process conjugate prior random finite set target tracking Bayesian estimation |
| url | http://hdl.handle.net/20.500.11937/4136 |