Multi-Scan Generalized Labeled Multi-Bernoulli Filter
| dc.contributor.author | Vo, Ba Tuong | |
| dc.contributor.author | Vo, Ba-Ngu | |
| dc.date.accessioned | 2018-12-13T09:11:24Z | |
| dc.date.available | 2018-12-13T09:11:24Z | |
| dc.date.created | 2018-12-12T02:46:41Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | Vo, B.T. and Vo, B. 2018. Multi-Scan Generalized Labeled Multi-Bernoulli Filter, pp. 195-202. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/71807 | |
| dc.identifier.doi | 10.23919/ICIF.2018.8455419 | |
| dc.description.abstract |
© 2018 ISIF This paper extends the generalized labeled multi-Bernoulli (GLMB) tracking filter to a batch multi-target tracker. In a labeled random finite set formulation, a multi-target tracking filter propagates the labeled multi-target filtering density while a batch multi-target tracker propagates the labeled multi-target posterior density. The GLMB filter is an analytic solution to the labeled multi-target filtering recursion. In this work, we show that the GLMB filter can be extended to an analytic multi-object posterior recursion. | |
| dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP170104854 | |
| dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP160104662 | |
| dc.title | Multi-Scan Generalized Labeled Multi-Bernoulli Filter | |
| dc.type | Conference Paper | |
| dcterms.source.startPage | 195 | |
| dcterms.source.endPage | 202 | |
| dcterms.source.title | 2018 21st International Conference on Information Fusion, FUSION 2018 | |
| dcterms.source.series | 2018 21st International Conference on Information Fusion, FUSION 2018 | |
| dcterms.source.isbn | 9780996452762 | |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Science (EECMS) | |
| curtin.accessStatus | Fulltext not available |
Files in this item
| Files | Size | Format | View |
|---|---|---|---|
|
There are no files associated with this item. |
|||