A variational Bayesian multiple particle filtering scheme for large-dimensional systems

Handle URI:
http://hdl.handle.net/10754/614418
Title:
A variational Bayesian multiple particle filtering scheme for large-dimensional systems
Authors:
Ait-El-Fquih, Boujemaa; Hoteit, Ibrahim ( 0000-0002-3751-4393 )
Abstract:
This paper considers the Bayesian filtering problem in high-dimensional nonlinear state-space systems. In such systems, classical particle filters (PFs) are impractical due to the prohibitive number of required particles to obtain reasonable performances. One approach that has been introduced to overcome this problem is the concept of multiple PFs (MPFs), where the state-space is split into low-dimensional subspaces and then a separate PF is applied to each subspace. Remarkable performances of MPF-like filters motivated our investigation here into a new strategy that combines the variational Bayesian approach to split the state-space with random sampling techniques, to derive a new computationally efficient MPF. The propagation of each particle in the prediction step of the resulting filter requires generating only a single particle in contrast with standard MPFs, for which a set of (children) particles is required. We present simulation results to evaluate the behavior of the proposed filter and compare its performances against standard PF and a MPF.
KAUST Department:
Applied Mathematics and Computational Science Program
Citation:
A variational Bayesian multiple particle filtering scheme for large-dimensional systems 2016:1 IEEE Transactions on Signal Processing
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Signal Processing
Issue Date:
14-Jun-2016
DOI:
10.1109/TSP.2016.2580524
Type:
Article
ISSN:
1053-587X; 1941-0476
Sponsors:
Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). We would like to thank three anonymous reviewers for their constructive comments and suggestions.
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7491275
Appears in Collections:
Articles

Full metadata record

DC FieldValue Language
dc.contributor.authorAit-El-Fquih, Boujemaaen
dc.contributor.authorHoteit, Ibrahimen
dc.date.accessioned2016-06-23T11:00:32Z-
dc.date.available2016-06-23T11:00:32Z-
dc.date.issued2016-06-14-
dc.identifier.citationA variational Bayesian multiple particle filtering scheme for large-dimensional systems 2016:1 IEEE Transactions on Signal Processingen
dc.identifier.issn1053-587X-
dc.identifier.issn1941-0476-
dc.identifier.doi10.1109/TSP.2016.2580524-
dc.identifier.urihttp://hdl.handle.net/10754/614418-
dc.description.abstractThis paper considers the Bayesian filtering problem in high-dimensional nonlinear state-space systems. In such systems, classical particle filters (PFs) are impractical due to the prohibitive number of required particles to obtain reasonable performances. One approach that has been introduced to overcome this problem is the concept of multiple PFs (MPFs), where the state-space is split into low-dimensional subspaces and then a separate PF is applied to each subspace. Remarkable performances of MPF-like filters motivated our investigation here into a new strategy that combines the variational Bayesian approach to split the state-space with random sampling techniques, to derive a new computationally efficient MPF. The propagation of each particle in the prediction step of the resulting filter requires generating only a single particle in contrast with standard MPFs, for which a set of (children) particles is required. We present simulation results to evaluate the behavior of the proposed filter and compare its performances against standard PF and a MPF.en
dc.description.sponsorshipResearch reported in this publication was supported by King Abdullah University of Science and Technology (KAUST). We would like to thank three anonymous reviewers for their constructive comments and suggestions.en
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7491275en
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.subjectBayesian filteringen
dc.subjectHidden Markov Chainen
dc.subjectHigh dimensionen
dc.subjectParticle filteringen
dc.subjectSequential Monte Carloen
dc.subjectVariational Bayesen
dc.titleA variational Bayesian multiple particle filtering scheme for large-dimensional systemsen
dc.typeArticleen
dc.contributor.departmentApplied Mathematics and Computational Science Programen
dc.identifier.journalIEEE Transactions on Signal Processingen
dc.eprint.versionPost-printen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorAit-El-Fquih, Boujemaaen
kaust.authorHoteit, Ibrahimen
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