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dc.contributor.authorPapi, Francesco
dc.contributor.authorBa-Ngu, V.
dc.contributor.authorBa-Tuong, V.
dc.contributor.authorFantacci, C.
dc.contributor.authorBeard, M.
dc.date.accessioned2017-01-30T12:43:29Z
dc.date.available2017-01-30T12:43:29Z
dc.date.created2016-03-14T19:30:23Z
dc.date.issued2015
dc.identifier.citationPapi, F. and Ba-Ngu, V. and Ba-Tuong, V. and Fantacci, C. and Beard, M. 2015. Generalized Labeled Multi-Bernoulli Approximation of Multi-Object Densities. A-PPP: Array-aided Precise Point Positioning with Global Navigation Satellites Systems. 63 (20): pp. 5487-5497.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/24520
dc.identifier.doi10.1109/TSP.2015.2454478
dc.description.abstract

In multi-object inference, the multi-object probability density captures the uncertainty in the number and the states of the objects as well as the statistical dependence between the objects. Exact computation of the multi-object density is generally intractable and tractable implementations usually require statistical independence assumptions between objects. In this paper we propose a tractable multi-object density approximation that can capture statistical dependence between objects. In particular, we derive a tractable Generalized Labeled Multi-Bernoulli (GLMB) density that matches the cardinality distribution and the first moment of the labeled multi-object distribution of interest. It is also shown that the proposed approximation minimizes the Kullback-Leibler divergence over a special tractable class of GLMB densities. Based on the proposed GLMB approximation we further demonstrate a tractable multi-object tracking algorithm for generic measurement models. Simulation results for a multi-object Track-Before-Detect example using radar measurements in low signal-to-noise ratio (SNR) scenarios verify the applicability of the proposed approach.

dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/DP130104404
dc.titleGeneralized Labeled Multi-Bernoulli Approximation of Multi-Object Densities
dc.typeJournal Article
dcterms.source.volume63
dcterms.source.number20
dcterms.source.startPage5487
dcterms.source.endPage5497
dcterms.source.issn1053-587X
dcterms.source.titleA-PPP: Array-aided Precise Point Positioning with Global Navigation Satellites Systems
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available


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