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An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation

Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to sol...

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Autores principales: Zhang, Xinyu, Ren, Mengjiao, Duan, Jiemin, Yi, Yingmin, Lei, Biyu, Wu, Shuyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383641/
https://www.ncbi.nlm.nih.gov/pubmed/37514896
http://dx.doi.org/10.3390/s23146603
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author Zhang, Xinyu
Ren, Mengjiao
Duan, Jiemin
Yi, Yingmin
Lei, Biyu
Wu, Shuyue
author_facet Zhang, Xinyu
Ren, Mengjiao
Duan, Jiemin
Yi, Yingmin
Lei, Biyu
Wu, Shuyue
author_sort Zhang, Xinyu
collection PubMed
description Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower.
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spelling pubmed-103836412023-07-30 An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation Zhang, Xinyu Ren, Mengjiao Duan, Jiemin Yi, Yingmin Lei, Biyu Wu, Shuyue Sensors (Basel) Article Although the cost-reference particle filter (CRPF) has a good advantage in solving the state estimation problem with unknown noise statistical characteristics, its estimation accuracy is still affected by the lack of particle diversity and sensitivity to the particles’ initial value. In order to solve these problems of the CRPF, this paper proposed an intelligent cost-reference particle filter algorithm based on multi-population cooperation. A multi-population cooperative resampling strategy based on ring structure was designed. The particles were divided into multiple independent populations upon initialization, and each population generated particles with a different initial distribution. The particles in each population were divided into three different particle sets with high, medium and low weights by the golden section ratio according to the weight. The particle sets with high and medium weights were retained. Then, a cooperative strategy based on Gaussian mutation was designed to resample the low-weight particle set of each population. The high-weight particles of the previous population in the ring structure were randomly selected for Gaussian mutation to replace the low-weight particles in the current population. The low-weight particles of all populations were resampled in turn. The simulation results show that the intelligent CRPF based on multi-population cooperation proposed in this paper can reduce the sensitivity of the CRPF to the particles’ initial value and improve the particle diversity in resampling. Compared with the general CRPF and intelligent CRPF with adaptive MH resampling (MH-CRPF), the RMSE and MAE of the proposed method are lower. MDPI 2023-07-22 /pmc/articles/PMC10383641/ /pubmed/37514896 http://dx.doi.org/10.3390/s23146603 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhang, Xinyu
Ren, Mengjiao
Duan, Jiemin
Yi, Yingmin
Lei, Biyu
Wu, Shuyue
An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title_full An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title_fullStr An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title_full_unstemmed An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title_short An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
title_sort intelligent cost-reference particle filter with resampling of multi-population cooperation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10383641/
https://www.ncbi.nlm.nih.gov/pubmed/37514896
http://dx.doi.org/10.3390/s23146603
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