A Fusion Kalman Filter and UFIR Estimator Using the Influence Function Method  

作　　者：Wei Xue Xiaoli Luan Shunyi Zhao Fei Liu 

机构地区：[1]IEEE [2]the Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education),Institute of Automation,Jiangnan University

出　　处：《IEEE/CAA Journal of Automatica Sinica》2022年第4期709-718,共10页自动化学报（英文版）

基　　金：supported in part by the National Natural Science Foundation of China(61973136,61991402,61833007);the Natural Science Foundation of Jiangsu Province(BK20211528)。

摘　　要：In this paper,the Kalman filter(KF)and the unbiased finite impulse response(UFIR)filter are fused in the discrete-time state-space to improve robustness against uncertainties.To avoid the problem where fusion filters may give up some advantages of UFIR filters by fusing based on noise statistics,we attempt to find a way to fuse without using noise statistics.The fusion filtering algorithm is derived using the influence function that provides a quantified measure for disturbances on the resulting filtering outputs and is termed as an influence finite impulse response(IFIR)filter.The main advantage of the proposed method is that the noise statistics of process noise and measurement noise are no longer required in the fusion process,showing that a critical feature of the UFIR filter is inherited.One numerical example and a practice-oriented case are given to illustrate the effectiveness of the proposed method.It is shown that the IFIR filter has adaptive performance and can automatically switch from the Kalman estimate to the UFIR estimates according to operating conditions.Moreover,the proposed method can reduce the effects of optimal horizon length on the UFIR estimate and can give the state estimates of best accuracy among all the compared methods.

关 键 词：Fusion filter influence function Kalman filter(KF) ROBUSTNESS unbiased finite impulse response(FIR) 

分 类 号：TN713[电子电信—电路与系统]