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State tracking of nonlinear system with multiplicative noises based on AR-SIS particle filter

  • Harbin Institute of Technology

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

Abstract

Under the framework of Bayesian filter, a sequential importance sampling particle filter based on the acceptance-rejection (AR) sampling is proposed for tracking the state of a nonlinear dynamic system. A set of samples is sequentially drawn to represent the posterior distribution of the state with the new observation at current time. In the existing filtering methods, the new observation is used only in the updating stage, not in the prediction stage. Our sampling method differs from the existing ones in that at the prediction stage the new observation is also used in order to evaluate the new weights of the samples. For illustration, the proposed particle filter is used to track the state of a nonlinear system with the multiplicative noise, which occurs in the measurement equation.

Original languageEnglish
Title of host publicationProceedings of the 28th Chinese Control and Decision Conference, CCDC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1747-1752
Number of pages6
ISBN (Electronic)9781467397148
DOIs
StatePublished - 3 Aug 2016
Event28th Chinese Control and Decision Conference, CCDC 2016 - Yinchuan, China
Duration: 28 May 201630 May 2016

Publication series

NameProceedings of the 28th Chinese Control and Decision Conference, CCDC 2016

Conference

Conference28th Chinese Control and Decision Conference, CCDC 2016
Country/TerritoryChina
CityYinchuan
Period28/05/1630/05/16

Keywords

  • Acceptance-Rejection (AR) Sampling
  • Bayesian Filter
  • Nonlinear System with Multiplicative Noises
  • Particle Filter (PF)
  • Sequential-Importance Sampling (SIS)

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