Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering

The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumpti...

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Veröffentlicht in:	IEEE transactions on systems, man, and cybernetics. Systems Jg. 51; H. 5; S. 3093 - 3102
Hauptverfasser:	Liu, Xi, Ren, Zhigang, Lyu, Hongqiang, Jiang, Zhihong, Ren, Pengju, Chen, Badong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.05.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Covariance matrices Criteria Extended Kalman filter Extended Kalman filter (EKF) fixed-point algorithm Iterative algorithms Iterative methods Kalman filters Kernel Linear regression Mathematical model maximum correntropy criterion (MCC) Noise measurement Nonlinear filters Optimization Performance degradation Random noise Regression models Tracking
ISSN:	2168-2216, 2168-2232
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Abstract	The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example.
AbstractList	The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly used in EKF is the celebrated minimum mean square error (MMSE) criterion, which exhibits excellent performance under Gaussian noise assumption. However, its performance may degrade dramatically when the noises are heavy tailed. To cope with this problem, this paper proposes two new nonlinear filters, namely the linear regression maximum correntropy EKF (LRMCEKF) and nonlinear regression maximum correntropy EKF (NRMCEKF), by applying the maximum correntropy criterion (MCC) rather than the MMSE criterion to EKF. In both filters, a regression model is formulated, and a fixed-point iterative algorithm is utilized to obtain the posterior estimates. The effectiveness and robustness of the proposed algorithms in target tracking are confirmed by an illustrative example.
Author	Lyu, Hongqiang Jiang, Zhihong Ren, Pengju Chen, Badong Liu, Xi Ren, Zhigang
Author_xml	– sequence: 1 givenname: Xi orcidid: 0000-0002-4659-8955 surname: Liu fullname: Liu, Xi email: lx1102@stu.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China – sequence: 2 givenname: Zhigang orcidid: 0000-0001-6862-3763 surname: Ren fullname: Ren, Zhigang email: renzg@mail.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China – sequence: 3 givenname: Hongqiang orcidid: 0000-0001-5061-4762 surname: Lyu fullname: Lyu, Hongqiang email: hongqianglv@mail.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China – sequence: 4 givenname: Zhihong orcidid: 0000-0001-8133-6312 surname: Jiang fullname: Jiang, Zhihong email: jiangzhihong@bit.edu.cn organization: School of Mechatronic Engineering, Beijing Institute of Technology, Beijing, China – sequence: 5 givenname: Pengju surname: Ren fullname: Ren, Pengju email: pengjuren@mail.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China – sequence: 6 givenname: Badong orcidid: 0000-0003-1710-3818 surname: Chen fullname: Chen, Badong email: chenbd@mail.xjtu.edu.cn organization: School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China
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Snippet	The extended Kalman filter (EKF) is a method extensively applied in many areas, particularly, in nonlinear target tracking. The optimization criterion commonly...
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SubjectTerms	Covariance matrices Criteria Extended Kalman filter Extended Kalman filter (EKF) fixed-point algorithm Iterative algorithms Iterative methods Kalman filters Kernel Linear regression Mathematical model maximum correntropy criterion (MCC) Noise measurement Nonlinear filters Optimization Performance degradation Random noise Regression models Tracking
Title	Linear and Nonlinear Regression-Based Maximum Correntropy Extended Kalman Filtering
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