An efficient multiple particle filter based on the variational Bayesian approach

Boujemaa Ait-El-Fquih, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper addresses the filtering problem in large-dimensional systems, in which conventional particle filters (PFs) remain computationally prohibitive owing to the large number of particles needed to obtain reasonable performances. To overcome this drawback, a class of multiple particle filters (MPFs) has been recently introduced in which the state-space is split into low-dimensional subspaces, and then a separate PF is applied to each subspace. In this paper, we adopt the variational Bayesian (VB) approach to propose a new MPF, the VBMPF. The proposed filter is computationally more efficient since the propagation of each particle requires generating one (new) particle only, while in the standard MPFs a set of (children) particles needs to be generated. In a numerical test, the proposed VBMPF behaves better than the PF and MPF.
Original languageEnglish (US)
Title of host publication2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages252-257
Number of pages6
ISBN (Print)9781509004812
DOIs
StatePublished - Feb 1 2016

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