A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update
This paper proposes a new implementation for the delta generalized labeled multi-Bernoulli (d-GLMB) filter by combining prediction and update into a single step. In contrast to the original implementation which requires different truncation procedures for each component in the prediction and update,...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2015
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| Online Access: | http://hdl.handle.net/20.500.11937/35397 |
| _version_ | 1848754486149906432 |
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| author | Hoang, H. Vo, Ba-Ngu Vo, Ba Tuong |
| author_facet | Hoang, H. Vo, Ba-Ngu Vo, Ba Tuong |
| author_sort | Hoang, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper proposes a new implementation for the delta generalized labeled multi-Bernoulli (d-GLMB) filter by combining prediction and update into a single step. In contrast to the original implementation which requires different truncation procedures for each component in the prediction and update, the joint strategy involves only one truncation per component in the filtering density, thus drastically reduces the number of computations. Performance comparison with the original implementation is presented through numerical studies. |
| first_indexed | 2025-11-14T08:41:10Z |
| format | Conference Paper |
| id | curtin-20.500.11937-35397 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:41:10Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-353972017-01-30T13:49:27Z A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update Hoang, H. Vo, Ba-Ngu Vo, Ba Tuong This paper proposes a new implementation for the delta generalized labeled multi-Bernoulli (d-GLMB) filter by combining prediction and update into a single step. In contrast to the original implementation which requires different truncation procedures for each component in the prediction and update, the joint strategy involves only one truncation per component in the filtering density, thus drastically reduces the number of computations. Performance comparison with the original implementation is presented through numerical studies. 2015 Conference Paper http://hdl.handle.net/20.500.11937/35397 restricted |
| spellingShingle | Hoang, H. Vo, Ba-Ngu Vo, Ba Tuong A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title | A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title_full | A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title_fullStr | A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title_full_unstemmed | A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title_short | A fast implementation of the generalized labeled multi-Bernoulli filter with joint prediction and update |
| title_sort | fast implementation of the generalized labeled multi-bernoulli filter with joint prediction and update |
| url | http://hdl.handle.net/20.500.11937/35397 |