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Particle filter algorithm based on geometric center and likelihood estimation

文献类型：期刊论文

作者Fang, Xing1; Luo, Yin2,3; Wang, Lei2,3; Jiang, ShuiBin2,3; Xu, Nan2,3; Zeng, Daniel Dajun3,4,5
刊名AIP ADVANCES
出版日期2021-08-01
卷号11期号:8页码:8
DOI10.1063/5.0058367
通讯作者Luo, Yin(yin.luo@wenge.com)
英文摘要The improved particle filter (PF) based on the geometric center and likelihood estimation is proposed in order to solve the problem of particle dilution and degradation. In the resampling stage, the geometric center is used to resample the particles. The particles are filtered according to the distance between the particles and the geometric center, and then the particles are resampled. The resampled particles are composed of newborn particles and non-resampled particles. The former can help alleviate the degradation problem, while the latter can keep the diversity of the particle set. In order to ensure effectiveness of the PF, the positioning error threshold of the particle filter is introduced. In the phase of particle weighting calculation, in view of the problem of low accuracy and divergence of PF state estimation caused by non-stationary and non-Gaussian noise, it adopts non-Gaussian noise parameter estimation based on likelihood to approximately estimate the measurement noise instead of the Gaussian density function. The proposed model is applied to particle weight calculation to avoid particle degradation caused by Gaussian density function approximation. The simulation results show that, after the improved algorithm, the root-mean-square error is reduced to 0.085, the variance is reduced to 0.014, and the running time is shortened by 14.8% compared with the polynomial resampling algorithm, which can effectively alleviate particle degradation and dilution in the traditional PF algorithm, and the positioning accuracy is also improved. (C) 2021 Author(s).
WOS关键词RESAMPLING METHODS ; NEURAL-NETWORK ; KALMAN FILTER ; LOCALIZATION
资助项目National Key R&D Program of China[2016QY02D0305] ; Key Research Program of the Chinese Academy of Sciences[ZDRW-XH-2017-3] ; National Natural Science Foundation of China[71621002] ; National Natural Science Foundation of China[61671450] ; National Key Research and Development Program of China[2020AAA0103405] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDA27030100]
WOS研究方向Science & Technology - Other Topics ; Materials Science ; Physics
语种英语
WOS记录号WOS:000692195300005
出版者AIP Publishing
资助机构National Key R&D Program of China ; Key Research Program of the Chinese Academy of Sciences ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; Strategic Priority Research Program of Chinese Academy of Sciences
源URL[http://ir.ia.ac.cn/handle/173211/45944]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Luo, Yin
作者单位1.Tianjin Univ, Sch Management & Econ, Beijing, Peoples R China
2.Wenge Technol Co Ltd, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing, Peoples R China
5.Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85001 USA
推荐引用方式
GB/T 7714		 Fang, Xing,Luo, Yin,Wang, Lei,et al. Particle filter algorithm based on geometric center and likelihood estimation[J]. AIP ADVANCES,2021,11(8):8.
APA Fang, Xing,Luo, Yin,Wang, Lei,Jiang, ShuiBin,Xu, Nan,&Zeng, Daniel Dajun.(2021).Particle filter algorithm based on geometric center and likelihood estimation.AIP ADVANCES,11(8),8.
MLA Fang, Xing,et al."Particle filter algorithm based on geometric center and likelihood estimation".AIP ADVANCES 11.8(2021):8.

入库方式： OAI收割

来源：自动化研究所

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