Two-stage Cubature Kalman Filtering Fusion Algorithms for Nonlinear Systems

被引:0
|
作者
Wang, Hong [1 ]
Niu, Zhuyun [2 ]
机构
[1] Hangzhou Dianzi Univ, Sch Automat, Inst Syst Sci & Control Engn, Hangzhou 310008, Peoples R China
[2] North Automat Control Technol Inst, Taiyuan 030000, Peoples R China
关键词
Bias Estimation; Cubature Kalman; Multi-sensor; Main Filter; Local Filter;
D O I
10.23919/chicc.2019.8866105
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The traditional two-stage Kalman filtering fusion method can not deal with the case of non-linear systems. which limits its application scope and level in practical engineering. Aiming at the above problems, this paper studies the design of two-stage cubature Kalman filter fusion algorithm for non-linear systems, and establishes a two-stage cubature Kalman filter fusion algorithm for multi-sensor systems. The main filter uses the output information of each local filter, fuses the global values, and feeds back to each local filter reasonably. The simulation results show that the proposed two-stage cubature Kalman filter fusion estimator (MTSCKF) has better performance than the two-stage cubature Kalman filter estimator (TSCKF) in filtering estimation accuracy.
引用
收藏
页码:3634 / 3638
页数:5
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