Adapting the number of particles in sequential Monte Carlo methods through an online scheme for convergence assessment

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dc.contributor.author Elvira Arregui, Víctor
dc.contributor.author Míguez Arenas, Joaquín
dc.contributor.author Djuric, Petar M.
dc.date.accessioned 2018-02-23T13:15:08Z
dc.date.available 2018-02-23T13:15:08Z
dc.date.issued 2016-12-08
dc.identifier.bibliographicCitation IEEE Transactions on Signal Processing, (2017), 65(7), 1781-1794.
dc.identifier.issn 1941-0476
dc.identifier.uri http://hdl.handle.net/10016/25898
dc.description.abstract Particle filters are broadly used to approximate posterior distributions of hidden states in state-space models by means of sets of weighted particles. While the convergence of the filter is guaranteed when the number of particles tends to infinity, the quality of the approximation is usually unknown but strongly dependent on the number of particles. In this paper, we propose a novel method for assessing the convergence of particle filters in an online manner, as well as a simple scheme for the online adaptation of the number of particles based on the convergence assessment. The method is based on a sequential comparison between the actual observations and their predictive probability distributions approximated by the filter. We provide a rigorous theoretical analysis of the proposed methodology and, as an example of its practical use, we present simulations of a simple algorithm for the dynamic and online adaptation of the number of particles during the operation of a particle filter on a stochastic version of the Lorenz 63 system.
dc.description.sponsorship This work was supported in part by the Ministerio de Economía y Competitividad of Spain under Grant TEC2013-41718-R OTOSiS, Grant TEC2012-38883-C02-01 COMPREHENSION, and Grant TEC2015-69868-C2-1-R ADVENTURE, in part by the Office of Naval Research Global under Grant N62909-15-1-2011, and in part by the National Science Foundation under Grant CCF-1320626 and Grant CCF-1618999.
dc.format.extent 13
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher IEEE
dc.rights 1053-587X © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications standards/publications/rights/index.html for more information.
dc.subject.other Particle filtering
dc.subject.other Sequential Monte Carlo
dc.subject.other Convergence assessment
dc.subject.other Predictive distribution
dc.subject.other Convergence analysis
dc.subject.other Computational complexity
dc.subject.other Adaptive complexity
dc.title Adapting the number of particles in sequential Monte Carlo methods through an online scheme for convergence assessment
dc.type article
dc.subject.eciencia Telecomunicaciones
dc.identifier.doi https://doi.org/10.1109/TSP.2016.2637324
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TEC2013-41718-R
dc.relation.projectID Gobierno de España. TEC2015-69868-C2-1-R
dc.relation.projectID Gobierno de España. TEC2012-38883-C02-01
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 1781
dc.identifier.publicationissue 7
dc.identifier.publicationlastpage 1794
dc.identifier.publicationtitle IEEE Transactions on Signal Processing
dc.identifier.publicationvolume 65
dc.identifier.uxxi AR/0000019428
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