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dc.contributor.authorBattistelli, G.
dc.contributor.authorChisci, L.
dc.contributor.authorFantacci, C.
dc.contributor.authorFarina, A.
dc.contributor.authorVo, Ba-Ngu
dc.identifier.citationBattistelli, G. and Chisci, L. and Fantacci, C. and Farina, A. and Vo, B. 2015. Average Kullback-Leibler divergence for random finite sets, Proceedings of the 18th International Conference on Information Fusion (Fusion), Jul 6-9 2015, pp. 1359-1366. Washington, DC: IEEE.

The paper deals with the fusion of multiobject information over a network of heterogeneous and geographically dispersed nodes with sensing, communication and processing capabilities. To exploit the benefits of sensor networks for multiobject estimation problems, like e.g. multitarget tracking and multirobot SLAM (Simultaneous Localization and Mapping), a key issue to be addressed is how to consistently fuse (average) locally updated multiobject densities. In this paper we discuss the generalization of Kullback-Leibler average, originally conceived for single-object densities (i.e. probability density functions) to (both unlabeled and labeled) multiobject densities. Then, with a view to develop scalable and reliable distributed multiobject estimation algorithms, we review approaches to iteratively compute, in each node of the network, the collective multiobject average via scalable and neighborwise computations.

dc.titleAverage Kullback-Leibler divergence for random finite sets
dc.typeConference Paper
dcterms.source.title2015 18th International Conference on Information Fusion, Fusion 2015
dcterms.source.series2015 18th International Conference on Information Fusion, Fusion 2015
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.accessStatusFulltext not available

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