Adaptive parallel/serial sampling mechanisms for particle filtering in dynamic bayesian networks



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Besada Portas, Eva and Cruz García, Jesús Manuel de la and Plis, Sergey M. and Lane, Terran (2010) Adaptive parallel/serial sampling mechanisms for particle filtering in dynamic bayesian networks. In Machine Learning and Knowledge Discovery in Databases, Pt I: European Conference, ECML PKDD 2010. Springer-Verlag Berlin, pp. 119-134. ISBN 978-3-642-15879-7

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Monitoring the variables of real world dynamical systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding probability distribution over the unobserved variables. However, they suffer from the curse of dimensionality problem: the necessary number of particles grows exponentially with the dimensionality of the hidden state space. The problem is aggravated when the initial distribution of the variables is not well known, as happens in global localization problems. In this paper we present two new adaptive sampling mechanisms for PFs for systems whose variable dependencies can be factored into a Dynamic Bayesian Network. The novel PFs, developed over the proposed sampling mechanisms, exploit the strengths of other existing PFs. Their adaptive mechanisms 1) modify or establish probabilistic links among the subspaces of hidden variables that are independently explored to build particles consistent with the current measurements and past history, and 2) tune the performance of the new PFs toward the behaviors of several existing PFs. We demonstrate their performance on some complex dynamical system estimation problems, showing that our methods successfully localize and track hidden states, and outperform some of the existing PFs.

Item Type:Book Section
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© Springer-Verlag Berlin Heidelberg 2010. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) SEP (2010. Barcelona, Spain)

Uncontrolled Keywords:Computer scienceI; Artificial intelligence
Subjects:Sciences > Computer science > Artificial intelligence
Sciences > Computer science
ID Code:21421
Deposited On:18 Jun 2013 08:44
Last Modified:31 Dec 2020 00:01

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