An Improved Particle Filter Based on UKF and Weight Optimization

被引：0

作者：

Hui, Zhao ^{[1
]}

Wang Lifen ^{[2
]}

Yuan, Ren ^{[2
]}

Geng Mengmeng ^{[1
]}

机构：

[1] Space Engn Univ, Grad Sch, Beijing, Peoples R China

[2] Space Engn Univ, Dept Aerosp Sci & Technol, Beijing, Peoples R China

来源：

2020 IEEE 3RD INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND SIGNAL PROCESSING (ICICSP 2020) | 2020年

关键词：

particle filtering; state estimation; weight optimization; suggested distribution function; particle depletion;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Aiming at the problem of limited efficiency and accuracy of state estimation in the case of non-linear and non-Gaussian systems, this paper proposes an improved particle filtering algorithm based on edge unscented Kalman filtering and weight optimization for the existing efficiency problems of UPF. Compared with traditional particle filtering, the improved filtering algorithm generates a suggested distribution function in order to avoid excessive variance of particle weights and combines the latest observation information to calculate a more efficient edgeless trace Kalman filter; during the resampling process The weight-optimized resampling method is introduced to solve the problem of particle depletion and improve particle diversity. It can be verified through theoretical derivation and simulation analysis that the improved algorithm effectively improves the calculation efficiency and has better estimation accuracy.

引用

页码：80 / 83

页数：4