Journal
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume 51, Issue 5, Pages 3093-3102Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSMC.2019.2917712
Keywords
Kalman filters; Covariance matrices; Kernel; Linear regression; Noise measurement; Iterative methods; Mathematical model; Extended Kalman filter (EKF); fixed-point algorithm; maximum correntropy criterion (MCC)
Funding
- 973 Program [2015CB351703]
- National Natural Science Foundation of China [61873199, 91648208]
- National Natural Science Foundation-Shenzhen Joint Research Program [U1613219]
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This paper proposes two new nonlinear filters, the LRMCEKF and NRMCEKF, which improve the performance of the extended Kalman filter by applying the maximum correntropy criterion instead of the minimum mean square error criterion. These filters demonstrate good performance and robustness in target tracking.
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.
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