A new nonlinear filter algorithm based on QMC quadrature

Dong Min Huang, Quan Pan

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

2 Scopus citations

Abstract

In order to avoid the possible gaps and clusters that arise from random sampling in Monte Carlo (MC) methods, and improve the sampling efficiency and calculation accuracy, the Quasi-Monte Carlo (QMC) methods are to be applied to replace it. The idea in QMC is to use more regularly distributed and deterministic points for sampling an integrand. We propose a new nonlinear filter by applying the QMC sampling methods to the particle filter algorithm. Given certain proposal distributions, a simulation example is presented. The results show that the nonlinear filter based on the QMC methods performs more efficient than that based on the MC methods. The performance provides some references for the real-time application of particle filter in nonlinear / non-Gaussian systems.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Pages190-193
Number of pages4
DOIs
StatePublished - 2008
EventInternational Conference on Computer Science and Software Engineering, CSSE 2008 - Wuhan, Hubei, China
Duration: 12 Dec 200814 Dec 2008

Publication series

NameProceedings - International Conference on Computer Science and Software Engineering, CSSE 2008
Volume3

Conference

ConferenceInternational Conference on Computer Science and Software Engineering, CSSE 2008
Country/TerritoryChina
CityWuhan, Hubei
Period12/12/0814/12/08

Keywords

  • Interval estimation
  • Low-discrepancy sequences
  • Monte Carlo
  • Particle filter
  • Quasi-Monte Carlo

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