A novel multiple-outlier-robust Kalman filter  

作　　者：Yulong HUANG Mingming BAI Yonggang ZHANG 

机构地区：[1]College of Intelligent Systems Science and Engineering,Harbin Engineering University,Harbin 150001,China

出　　处：《Frontiers of Information Technology & Electronic Engineering》2022年第3期422-437,共16页信息与电子工程前沿（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.61903097 and 61773133)。

摘　　要：This paper presents a novel multiple-outlier-robust Kalman filter(MORKF)for linear stochastic discretetime systems.A new multiple statistical similarity measure is first proposed to evaluate the similarity between two random vectors from dimension to dimension.Then,the proposed MORKF is derived via maximizing a multiple statistical similarity measure based cost function.The MORKF guarantees the convergence of iterations in mild conditions,and the boundedness of the approximation errors is analyzed theoretically.The selection strategy for the similarity function and comparisons with existing robust methods are presented.Simulation results show the advantages of the proposed filter.

关 键 词：Kalman filtering Multiple statistical similarity measure Multiple outliers Fixed-point iteration State estimate 

分 类 号：TN713[电子电信—电路与系统]