Full Text:   <5162>

Summary:  <348>

CLC number: TP273

On-line Access: 2022-03-22

Received: 2020-11-17

Revision Accepted: 2022-04-22

Crosschecked: 2021-03-01

Cited: 0

Clicked: 5742

Citations:  Bibtex RefMan EndNote GB/T7714


Yulong HUANG


Mingming BAI


Yonggang ZHANG


-   Go to

Article info.
Open peer comments

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.

@article{title="A novel multiple-outlier-robust Kalman filter",
author="Yulong HUANG, Mingming BAI, Yonggang ZHANG",
journal="Frontiers of Information Technology & Electronic Engineering",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T A novel multiple-outlier-robust Kalman filter
%A Yulong HUANG
%A Mingming BAI
%A Yonggang ZHANG
%J Frontiers of Information Technology & Electronic Engineering
%V 23
%N 3
%P 422-437
%@ 2095-9184
%D 2022
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2000642

T1 - A novel multiple-outlier-robust Kalman filter
A1 - Yulong HUANG
A1 - Mingming BAI
A1 - Yonggang ZHANG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 23
IS - 3
SP - 422
EP - 437
%@ 2095-9184
Y1 - 2022
PB - Zhejiang University Press & Springer
ER -
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


[1]Chen BD, Liu X, Zhao HQ, et al., 2017. Maximum correntropy Kalman filter. Automatica, 76:70-77.

[2]Huang YL, Zhang YG, Li N, et al., 2016. Robust Student’s t based nonlinear filter and smoother. IEEE Trans Aerosp Electron Syst, 52(5):2586-2596.

[3]Huang YL, Zhang YG, Li N, et al., 2017. A novel robust Student’s t-based Kalman filter. IEEE Trans Aerosp Electron Syst, 53(3):1545-1554.

[4]Huang YL, Zhang YG, Zhao YX, et al., 2019a. A novel robust Gaussian-Student’s t mixture distribution based Kalman filter. IEEE Trans Signal Process, 67(13):3606-3620.

[5]Huang YL, Zhang YG, Chambers JA, 2019b. A novel Kullback–Leibler divergence minimization-based adaptive Student’s t-filter. IEEE Trans Signal Process, 67(20):5417-5432.

[6]Huang YL, Zhang YG, Shi P, et al., 2019c. Robust Kalman filters based on Gaussian scale mixture distributions with application to target tracking. IEEE Trans Syst Man Cybern Syst, 49(10):2082-2096.

[7]Huang YL, Zhang YG, Zhao YX, et al., 2020. A novel outlier-robust Kalman filtering framework based on statistical similarity measure. IEEE Trans Autom Contr, 66(6):2677-2692.

[8]Huber PJ, 2011. Robust statistics. In: Lovric M (Ed.), International Encyclopedia of Statistical Science. Springer, Berlin, Germany.

[9]Piché R, Särkkä S, Hartikainen J, 2012. Recursive outlier-robust filtering and smoothing for nonlinear systems using the multivariate Student-t distribution. Proc IEEE Int Workshop on Machine Learning for Signal Processing, p.1-6.

[10]Roth M, Ardeshiri T, Özkan E, et al., 2017. Robust Bayesian filtering and smoothing using Student’s t distribution. https://arxiv.org/abs/1703.02428

[11]Simon D, 2006. Optimal State Estimation: Kalman, H, and Nonlinear Approaches. John Wiley, Hoboken, USA.

[12]Ting JA, Theodorou E, Schaal S, 2007. Learning an outlier-robust Kalman filter. Proc 18th European Conf on Machine Learning, p.748-756.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE