On the long-term stability of bootstrap-type particle filters

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Abstract

In this paper we discuss optimal filtering in general state-space models (SSMs) and present novel theoretical results on the long-term stability of bootstrap-type particle filters. More specifically, we establish that the asymptotic variance of the particle estimates is uniformly bounded in time. On the contrary to most previous results of this type, which in general presuppose that the state space of the hidden state process is compact (an assumption that is rarely satisfied in practice), our very mild assumptions are satisfied for a large class of SSMs with possibly non-compact state space. In addition, we derive similar stability results for the Lp error of the particle estimates. Importantly, our results hold for misspecified models, i.e., we do not at all assume that the data entering into the particle filter originate from the model governing the dynamics of the particles or not even from an SSM.

Original languageEnglish
Title of host publicationSYSID 2012 - 16th IFAC Symposium on System Identification, Final Program
PublisherIFAC Secretariat
Pages1131-1136
Number of pages6
EditionPART 1
ISBN (Print)9783902823069
DOIs
Publication statusPublished - 1 Jan 2012
EventUniversite Libre de Bruxelles - Bruxelles, Belgium
Duration: 11 Jul 201213 Jul 2012

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
Volume16
ISSN (Print)1474-6670

Conference

ConferenceUniversite Libre de Bruxelles
Country/TerritoryBelgium
CityBruxelles
Period11/07/1213/07/12

Keywords

  • Long-term stability
  • Particle filter
  • Sequential Monte Carlo
  • State-space models
  • Uniform convergence

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