An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
© 2017 International Society of Information Fusion (ISIF). This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering...
| Main Authors: | , |
|---|---|
| Format: | Conference Paper |
| Published: |
2017
|
| Online Access: | http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/63168 |
| _version_ | 1848761012914749440 |
|---|---|
| author | Vo, Ba-Ngu Vo, Ba Tuong |
| author_facet | Vo, Ba-Ngu Vo, Ba Tuong |
| author_sort | Vo, Ba-Ngu |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2017 International Society of Information Fusion (ISIF). This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a complexity in the order of the product of the number of measurements from each sensor, and quadratic in the number of hypothesized objects. |
| first_indexed | 2025-11-14T10:24:55Z |
| format | Conference Paper |
| id | curtin-20.500.11937-63168 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:24:55Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-631682022-10-27T07:18:57Z An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling Vo, Ba-Ngu Vo, Ba Tuong © 2017 International Society of Information Fusion (ISIF). This paper proposes an efficient implementation of the multi-sensor generalized labeled multi-Bernoulli (GLMB) filter. The solution exploits the GLMB joint prediction and update together with a new technique for truncating the GLMB filtering density based on Gibbs sampling. The resulting algorithm has a complexity in the order of the product of the number of measurements from each sensor, and quadratic in the number of hypothesized objects. 2017 Conference Paper http://hdl.handle.net/20.500.11937/63168 10.23919/ICIF.2017.8009647 http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP130104404 restricted |
| spellingShingle | Vo, Ba-Ngu Vo, Ba Tuong An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title_full | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title_fullStr | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title_full_unstemmed | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title_short | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling |
| title_sort | implementation of the multi-sensor generalized labeled multi-bernoulli filter via gibbs sampling |
| url | http://purl.org/au-research/grants/arc/DP170104854 http://purl.org/au-research/grants/arc/DP170104854 http://hdl.handle.net/20.500.11937/63168 |