Average Kullback-Leibler divergence for random finite sets
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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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Fantacci, C.; Vo, Ba-Ngu; Vo, Ba Tuong; Battistelli, G.; Chisci, L. (2018)© 1994-2012 IEEE. 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 ...
Papi, Francesco; Ba-Ngu, V.; Ba-Tuong, V.; Fantacci, C.; Beard, M. (2015)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 ...
Vo, Ba Tuong (2012)In Bayesian multi-object filtering, in contrast to Bayesian single object filtering, the number and the individual states of objects are to be determined in the presence noise, detection uncertainty and false alarms. The ...