Robust Fusion for Multisensor Multiobject Tracking
dc.contributor.author | Fantacci, C. | |
dc.contributor.author | Vo, Ba-Ngu | |
dc.contributor.author | Vo, Ba Tuong | |
dc.contributor.author | Battistelli, G. | |
dc.contributor.author | Chisci, L. | |
dc.date.accessioned | 2018-05-18T07:58:14Z | |
dc.date.available | 2018-05-18T07:58:14Z | |
dc.date.created | 2018-05-18T00:23:15Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | Fantacci, C. and Vo, B. and Vo, B.T. and Battistelli, G. and Chisci, L. 2018. Robust Fusion for Multisensor Multiobject Tracking. IEEE Signal Processing Letters. 25 (5): pp. 640-644. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/67376 | |
dc.identifier.doi | 10.1109/LSP.2018.2811750 | |
dc.description.abstract |
This letter proposes analytical expressions for the fusion of certain classes of labeled multiobject densities via Kullback-Leibler averaging. Specifically, we provide analytical fusion rules for the labeled multi-Bernoulli and marginalized d-generalized labeled multi-Bernoulli families of labeled multiobject densities. Information fusion via Kullback-Leibler averaging ensures immunity to double counting of information and is essential to the development of effective multiagent multiobject estimation. | |
dc.publisher | Institute of Electrical and Electronics Engineers | |
dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP160104662 | |
dc.title | Robust Fusion for Multisensor Multiobject Tracking | |
dc.type | Journal Article | |
dcterms.source.volume | 25 | |
dcterms.source.number | 5 | |
dcterms.source.startPage | 640 | |
dcterms.source.endPage | 644 | |
dcterms.source.issn | 1070-9908 | |
dcterms.source.title | IEEE Signal Processing Letters | |
curtin.department | School of Electrical Engineering, Computing and Mathematical Science (EECMS) | |
curtin.accessStatus | Fulltext not available |
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