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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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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. |
format | Online Article Text |
id | pubmed-10383641 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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