Average Kullback-Leibler divergence for random finite sets
MetadataShow full item record
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.
Showing items related by title, author, creator and subject.
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 ...
Long, Q.; Wu, Changzhi; Huang, T.; Wang, Xiangyu (2015)In this paper, we propose a genetic algorithm for unconstrained multi-objective optimization. Multi-objective genetic algorithm (MOGA) is a direct method for multi-objective optimization problems. Compared to the traditional ...
Beard, M.; Vo, Ba Tuong; Vo, Ba-Ngu (2016)This paper presents an analytical form for a multi-object smoother, based on a multi-object model known as the generalised labelled multi-Bernoulli (GLMB). The proposed smoother is based on the forward-backward smoothing ...