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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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 51; číslo 5; s. 3093 - 3102
Hlavní autoři: Liu, Xi, Ren, Zhigang, Lyu, Hongqiang, Jiang, Zhihong, Ren, Pengju, Chen, Badong
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2019.2917712