A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers

SUN Xiaohui; WEN Chenglin; WEN Tao

doi:10.1049/cje.2021.02.005

Volume 30 Issue 2

Apr. 2021

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SUN Xiaohui, WEN Chenglin, WEN Tao, “A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers,” Chinese Journal of Electronics, vol. 30, no. 2, pp. 313-321, 2021, doi: 10.1049/cje.2021.02.005

Citation:

SUN Xiaohui, WEN Chenglin, WEN Tao, “A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers,” Chinese Journal of Electronics, vol. 30, no. 2, pp. 313-321, 2021, doi: 10.1049/cje.2021.02.005

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A Novel Step-by-Step High-Order Extended Kalman Filter Design for a Class of Complex Systems with Multiple Basic Multipliers

doi: 10.1049/cje.2021.02.005

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

he National Natural Science Foundation of China 61751304

he National Natural Science Foundation of China 61806064

he National Natural Science Foundation of China 6193301

he National Natural Science Foundation of China 61703385

he National Natural Science Foundation of China U1664264

Science and Technology Project of China Electric Power Research Institute SGHB0000KXJS1800375

More Information

Author Bio:
SUN Xiaohui was born in 1993. She received B.E. degree from Harbin University of Science and Technology. She studied for a M.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)

WEN Chenglin was born in 1963. He graduated from Henan University in 1986, graduated from Zhengzhou University with a M.S. 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: WEN Tao (corresponding author) received the B.E. degree in computer science from Hangzhou Dianzi University in 2011, the M.S. 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 dependability improvement, wireless signal processing, and digital filter research (Email: wentao@bjtu.edu.cn)
Received Date: 2020-08-11
Accepted Date: 2020-10-28
Publish Date: 2021-03-01

Abstract

Abstract

A novel step-by-step linearization highorder Extended Kalman filter SH-EKF is designed for a class of nonlinear systems composed of linear functions and the product of several separable basic functions. The basic functions in the state and measurement models are defined as latent variables; the state and measurement models are equivalently formulated into pseudo-linear models based on the combination of the original variable and the latent variables; latent variables are regarded as new variables, and a dynamic linear model between each latent variable and other latent variables with original state is established; the measurement model is rewritten into the first-order linear product form between the current state and each latent variable; latent variables are solved by Kalman filter step by step, and a stepwise linearized high-order extended Kalman filter is designed. Illustration examples are presented to demonstrate the effectiveness of the new algorithm.
- High-order extended Kalman filter,
- Nonlinear systems,
- Latent variables,
- Pseudo-linear,
- Basic functions

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References(22)

References

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