An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling
| dc.contributor.author | Vo, Ba-Ngu | |
| dc.contributor.author | Vo, Ba Tuong | |
| dc.date.accessioned | 2018-02-06T06:15:45Z | |
| dc.date.available | 2018-02-06T06:15:45Z | |
| dc.date.created | 2018-02-06T05:49:57Z | |
| dc.date.issued | 2017 | |
| dc.identifier.citation | Vo, B. and Vo, B.T. 2017. An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/63168 | |
| dc.identifier.doi | 10.23919/ICIF.2017.8009647 | |
| dc.description.abstract |
© 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. | |
| dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP170104854 | |
| dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP130104404 | |
| dc.title | An implementation of the multi-sensor generalized labeled multi-Bernoulli filter via Gibbs sampling | |
| dc.type | Conference Paper | |
| dcterms.source.title | 20th International Conference on Information Fusion, Fusion 2017 - Proceedings | |
| dcterms.source.series | 20th International Conference on Information Fusion, Fusion 2017 - Proceedings | |
| dcterms.source.isbn | 9780996452700 | |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Science (EECMS) | |
| curtin.accessStatus | Fulltext not available |
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