摘要
为了改善传统容积卡尔曼滤波器(CKF)的滤波性能,将高阶容积卡尔曼滤波算法与非线性H∞鲁棒滤波算法相结合,提出一种自适应高阶容积H∞滤波算法(AHCHF)。该算法引入数值稳定性更强的奇异值分解方法(SVD)替换传统的Cholesky分解;同时将H∞鲁棒滤波的思想应用于高阶容积卡尔曼滤波;并基于新息与约束水平反比的关系,对约束水平γ值进行自适应选取,兼顾了滤波精度和系统的鲁棒性。仿真结果表明:相比于传统CKF算法和高阶CKF算法,AHCHF算法具有更高的滤波精度和鲁棒性。
For purpose of improving filter performance of the conventional cubature Kalman filter(CKF),a self-adaptive high-order cubature H∞ filtering algorithm(AHCHF)which having the high-order cubature Kalman filter(HCKF)combined with H∞ robust filtering algorithm was proposed.This algorithm adopts the singular value decomposition(SVD)with stronger numerical stability to replace traditional Cholesky decomposition,including the application of H∞ robust filter to the high-order cubature Kalman filtering.Basing on the inverse proportional relationship established between constraint level and filter information under nonlinear condition,the constraint level γ was selected adaptively and meanwhile,both filtering accuracy and system robustness were considered.The simulation results show that,as compared to the traditional CKF algorithm and the high-order CKF algorithm,this algorithm proposed has better filtering performance.
作者
张丹威
王晓东
黄国勇
包俊
ZHANG Dan-wei;WANG Xiao-dong;HUANG Guo-yong;BAO Jun(Faculty of Information Engineering & Automation,Key Laboratory for Measurement Control and Optimization of Complex Industrial Process,Kunming University of Science and Technology)
出处
《化工自动化及仪表》
CAS
2019年第5期333-339,共7页
Control and Instruments in Chemical Industry
基金
云南省重大科技专项(2015ZC005)
关键词
高阶容积卡尔曼滤波
H∞滤波
自适应
目标跟踪
滤波精度
鲁棒性
high-order cubature Kalman filtering
H∞ filtering
adaptive
target tracking
filtering accuracy
robustness