An Intelligent Cost-Reference Particle Filter with Resampling of Multi-Population Cooperation
Author:
Zhang Xinyu12ORCID,
Ren Mengjiao12,
Duan Jiemin12,
Yi Yingmin12,
Lei Biyu12,
Wu Shuyue12
Affiliation:
1. Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing, Xi’an University of Technology, Xi’an 710048, China
2. Faculty of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
Abstract
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.
Funder
National Natural Science Foundation of China
Natural Science Basic Research Plan in Shaanxi Province of China
Science and Technology Innovation Team of Shaanxi Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference26 articles.
1. Remaining useful life prediction of lithium-ion batteries based on conditional vibrational auto encoders-particle filter;Jiao;IEEE Trans. Instrum. Meas.,2020
2. Fault and state estimation for discrete linear variable parameter systems with integral measurement and delay;Qiao;Control Theory Appl.,2021
3. Research on ground multi-target guidance method of UAV group cooperative tracking;Niu;Sci. China (Tech. Sci.),2020
4. On sequential monte Carlo sampling methods for bayesian filtering;Doucet;Stat. Comput.,2000
5. Second order central difference particle filter Fast SLAM algorithm;Dai;Control Theory Appl.,2018
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