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A Gaussian Distributed Resampling Algorithm for Mitigation of Sample Impoverishment in Particle Filters

Authors
Choi, Hyun DuckPak, Jung MinLim, Myo TaegSong, Moon Kyou
Issue Date
8월-2015
Publisher
INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
Keywords
Gaussian distributed resampling; nonlinear filter; particle filter; state estimation
Citation
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.13, no.4, pp.1032 - 1036
Indexed
SCIE
SCOPUS
KCI
Journal Title
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
Volume
13
Number
4
Start Page
1032
End Page
1036
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/92791
DOI
10.1007/s12555-014-0355-2
ISSN
1598-6446
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.
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공과대학 (전기전자공학부)
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