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    Average Kullback-Leibler divergence for random finite sets

    Access Status
    Fulltext not available
    Authors
    Battistelli, G.
    Chisci, L.
    Fantacci, C.
    Farina, A.
    Vo, Ba-Ngu
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Battistelli, 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.
    Source Title
    2015 18th International Conference on Information Fusion, Fusion 2015
    ISBN
    9780982443866
    School
    Department of Electrical and Computer Engineering
    URI
    http://hdl.handle.net/20.500.11937/37829
    Collection
    • Curtin Research Publications
    Abstract

    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.

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