A Gaussian distributed resampling algorithm for mitigation of sample impoverishment in particle filters

Hyun Duck Choi, Jung Min Pak, Myo Taeg Lim, Moon Kyou Song

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

This paper proposes a new resampling algorithm, a Gaussian distributed resampling (GDR) algorithm, in order to solve the sample impoverishment problem in particle filters. The key concept of the proposed algorithm is to generate new particles on the basis of a Gaussian distribution, which depends on the size of the weights in the resampling process. In comparison with established resampling algorithms, particle diversity can be maintained, and thus the proposed algorithm avoids sample impoverishment. The proposed GDR algorithm guarantees a reliable estimation even if the number of samples is sharply reduced. Thus, the computational burden of particle filters can be reduced efficiently with the proposed GDR algorithm.

Original languageEnglish
Pages (from-to)1032-1036
Number of pages5
JournalInternational Journal of Control, Automation and Systems
Volume13
Issue number4
DOIs
Publication statusPublished - 2015 Aug 8

Keywords

  • Gaussian distributed resampling
  • nonlinear filter
  • particle filter
  • state estimation

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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