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

1 Citation (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

Fingerprint

Parallel algorithms
Gaussian distribution

Keywords

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

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

Cite this

A Gaussian distributed resampling algorithm for mitigation of sample impoverishment in particle filters. / Choi, Hyun Duck; Pak, Jung Min; Lim, Myo Taeg; Song, Moon Kyou.

In: International Journal of Control, Automation and Systems, Vol. 13, No. 4, 08.08.2015, p. 1032-1036.

Research output: Contribution to journalArticle

@article{8e055284616c434183c720f8a31e3ddd,
title = "A Gaussian distributed resampling algorithm for mitigation of sample impoverishment in particle filters",
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.",
keywords = "Gaussian distributed resampling, nonlinear filter, particle filter, state estimation",
author = "Choi, {Hyun Duck} and Pak, {Jung Min} and Lim, {Myo Taeg} and Song, {Moon Kyou}",
year = "2015",
month = "8",
day = "8",
doi = "10.1007/s12555-014-0355-2",
language = "English",
volume = "13",
pages = "1032--1036",
journal = "International Journal of Control, Automation and Systems",
issn = "1598-6446",
publisher = "Institute of Control, Robotics and Systems",
number = "4",

}

TY - JOUR

T1 - A Gaussian distributed resampling algorithm for mitigation of sample impoverishment in particle filters

AU - Choi, Hyun Duck

AU - Pak, Jung Min

AU - Lim, Myo Taeg

AU - Song, Moon Kyou

PY - 2015/8/8

Y1 - 2015/8/8

N2 - 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.

AB - 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.

KW - Gaussian distributed resampling

KW - nonlinear filter

KW - particle filter

KW - state estimation

UR - http://www.scopus.com/inward/record.url?scp=84938749097&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84938749097&partnerID=8YFLogxK

U2 - 10.1007/s12555-014-0355-2

DO - 10.1007/s12555-014-0355-2

M3 - Article

AN - SCOPUS:84938749097

VL - 13

SP - 1032

EP - 1036

JO - International Journal of Control, Automation and Systems

JF - International Journal of Control, Automation and Systems

SN - 1598-6446

IS - 4

ER -