A new particle filtering algorithm with structurally optimal importance function

Boujemaa Ait El Fquih*, François Deshouvries

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Bayesian estimation in nonlinear stochastic dynamical systems has been addressed for a long time. Among other solutions, Particle Filtering (PF) algorithms propagate in time a Monte Carlo (MC) approximation of the a posteriori filtering measure. However, a drawback of the classical PF algorithms is that the optimal conditional importance distribution (CID) is often difficult (or even impossible) to compute and to sample from. As a consequence, suboptimal sampling strategies have been proposed in the literature. In this paper we bypass this difficulty by rather considering the prediction sequential importance sampling (SIS) problem; the filtering MC approximation is obtained as a byproduct. The advantage of this prediction-PF method is that it combines optimality and simplicity, since for the prediction problem, the optimal CID happens to be the prior transition of the underlying Markov Chain (MC), from which it is often simple to sample from.

Original languageEnglish (US)
Title of host publication2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3413-3416
Number of pages4
ISBN (Print)1424414849, 9781424414840
DOIs
StatePublished - Jan 1 2008
Event2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP - Las Vegas, NV, United States
Duration: Mar 31 2008Apr 4 2008

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2008 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP
CountryUnited States
CityLas Vegas, NV
Period03/31/0804/4/08

Keywords

  • Hidden Markov chains
  • Optimal importance function
  • Particle filtering
  • Sequential importance sampling

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Fingerprint

Dive into the research topics of 'A new particle filtering algorithm with structurally optimal importance function'. Together they form a unique fingerprint.

Cite this