Adaptive Kalman Filtering Based on Model Parameter Ratios

被引：4

作者：

Ge, Quanbo ^{[1
]}

Li, Yunyu ^{[2
]}

Wang, Yuanliang ^{[3
]}

Hu, Xiaoming ^{[4
]}

Li, Hong ^{[5
]}

Sun, Changyin ^{[6
]}

机构：

[1] Nanjing Univ Informat Sci & Technol, Sch Automat, Nanjing 210044, Peoples R China

[2] Hangzhou Dianzi Univ, Inst Syst Sci & Control Engn, Sch Automat, Hangzhou 310018, Peoples R China

[3] Shanghai Maritime Univ, Sch Logist Engn, Shanghai 200135, Peoples R China

[4] KTH Royal Inst Technol, Stockholm 10044, Sweden

[5] Chinese Flight Test Estab, Inst Testing, Xian 710089, Peoples R China

[6] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2024年 / 69卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Noise measurement; Kalman filters; Q measurement; Estimation; Adaptation models; Covariance matrices; Time measurement; Estimation error; inaccurate models; Kalman filter (KF); model parameter ratio (MPR); particle swarm optimization (PSO); RANKING;

D O I：

10.1109/TAC.2024.3376306

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This article studies an adaptive Kalman filter method based on model parameter ratio. The model parameter ratio theory is proposed for the first time, and the adaptive estimation problem is transformed into a constrained optimization problem. Compared with the existing Sage-Husa adaptive filtering algorithm, it can be seen that the application of this theory can more accurately estimate the process noise covariance and measurement noise covariance matrix, so that the algorithm has better filtering accuracy and better state estimation performance, At the same time, it is also better in antidivergence and sensitivity to initial conditions.

引用

页码：6230 / 6237

页数：8

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