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CLC number: TP273

On-line Access: 2022-03-22

Received: 2020-11-17

Revision Accepted: 2022-04-22

Crosschecked: 2021-03-01

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Citations:  Bibtex RefMan EndNote GB/T7714


Yulong HUANG


Mingming BAI


Yonggang ZHANG


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Frontiers of Information Technology & Electronic Engineering  2022 Vol.23 No.3 P.422-437


A novel multiple-outlier-robust Kalman filter

Author(s):  Yulong HUANG, Mingming BAI, Yonggang ZHANG

Affiliation(s):  College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China

Corresponding email(s):   heuedu@163.com, mingming.bai@hrbeu.edu.cn, zhangyg@hrbeu.edu.cn

Key Words:  Kalman filtering, Multiple statistical similarity measure, Multiple outliers, Fixed-point iteration, State estimate

Yulong HUANG, Mingming BAI, Yonggang ZHANG. A novel multiple-outlier-robust Kalman filter[J]. Frontiers of Information Technology & Electronic Engineering, 2022, 23(3): 422-437.

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%A Mingming BAI
%A Yonggang ZHANG
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A1 - Yulong HUANG
A1 - Mingming BAI
A1 - Yonggang ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
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EP - 437
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.2000642

This paper presents a novel multiple-outlier-robust Kalman filter (MORKF) for linear stochastic discrete-time 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.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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