Maximum Correntropy High-Order Extended Kalman Filter

SUN Xiaohui; WEN Chenglin; WEN Tao

doi:10.1049/cje.2020.00.334

Volume 31 Issue 1

Jan. 2022

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SUN Xiaohui, WEN Chenglin, WEN Tao, “Maximum Correntropy High-Order Extended Kalman Filter,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 190-198, 2022, doi: 10.1049/cje.2020.00.334

Citation:

SUN Xiaohui, WEN Chenglin, WEN Tao, “Maximum Correntropy High-Order Extended Kalman Filter,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 190-198, 2022, doi: 10.1049/cje.2020.00.334

SUN Xiaohui, WEN Chenglin, WEN Tao, “Maximum Correntropy High-Order Extended Kalman Filter,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 190-198, 2022, doi: 10.1049/cje.2020.00.334

Citation:

SUN Xiaohui, WEN Chenglin, WEN Tao, “Maximum Correntropy High-Order Extended Kalman Filter,” Chinese Journal of Electronics, vol. 31, no. 1, pp. 190-198, 2022, doi: 10.1049/cje.2020.00.334

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Maximum Correntropy High-Order Extended Kalman Filter

doi: 10.1049/cje.2020.00.334

SUN Xiaohui^1
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WEN Chenglin^{1, 2
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WEN Tao^3
,

1.
School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
2.
School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, China
3.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China

Funds: This work was supported by the National Natural Science Foundation of China (61751304, 61806064, 61933013, 61703385, U1664264) and Science and Technology Project of China Electric Power Research Institute (SGHB0000KXJS1800375)

More Information

Author Bio:
was born in 1993. She received B.E. degree from Harbin University of Science and Technology. She studied for a master’s degree and a Ph.D. in Hangzhou Dianzi University in 2016 and 2018, respectively, and is currently a Ph.D. candidate. Her research interest is filter design. (Email: sun_xh1993@163.com)

was born in 1963. He graduated from Henan University in 1986, graduated from Zhengzhou University with a master degree in 1993 and received a Ph.D. from Northwestern Polytechnical University 1999. He went out of the Postdoctoral Mobile Station of Control Science and Engineering of Tsinghua University in 2002. He is a Professor of Hangzhou Dianzi University and Guangdong University of Petrochemical Technology. His research interests include information fusion and target detection, fault diagnosis and active security control, deep learning and optimization decision-making systems, cyberspace security and attack detection and positioning. (Email: wencl@hdu.edu.cn)

(corresponding author) received the B.Eng. degree in computer science from Hangzhou Dianzi University in 2011, the M.Sc. degree from the University of Bristol, Bristol, U.K. in 2013, and the Ph.D. degree from the Birmingham Centre for Railway Research and Education, University of Birmingham, Birmingham, U.K. in 2018. He currently works in the School of Electronic and Information Engineering, Beijing Jiaotong University. His research interests include CBTC system optimization, railway signaling simulation, railway depend-ability improvement, wireless signal processing, and digital filter research (Email: wentao@bjtu.edu.cn)
Received Date: 2020-10-10
Accepted Date: 2020-12-03

Available Online: 2021-09-22

Publish Date: 2022-01-05

Abstract

Abstract

In this paper, a novel maximum correntropy high-order extended Kalman filter (H-MCEKF) is proposed for a class of nonlinear non-Gaussian systems presented by polynomial form. All high-order polynomial terms in the state model are defined as implicit variables and regarded as parameter variables; the original state model is equivalently formulated into a pseudo-linear form with original variables and parameter variables; the dynamic relationship between each implicit variable and all variables is modeled, then an augmented linear state model appears by combing with pseudo-linear state model; similarly, the nonlinear measurement model can be equivalently rewritten into linear form; once again, the statistical characteristics of non-Gaussian modeling error are described by mean value and variance based on their finite samples; combing original measurement model with predicted value regarded as added state measurement, a cost function to solve the state estimation based on maximum correntropy criterion (MCC) is constructed; on the basis of this cost function, the state estimation problem can be equivalently converted into a recursive solution problem in the form of Kalman filter, in which the filter gain matrix is solved by numerical iteration though its fixed-point equation; illustration examples are presented to demonstrate the effectiveness of the new algorithm.
- Maximum correntropy criterion,
- Implicit variables,
- High-order extended Kalman filter,
- Fixed-point equation

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References(17)

References

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