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• W4281567319 startingPage "110410" @default.
• W4281567319 abstract "The Unscented Kalman filter (UKF) based on maximum correntropy criterion (MCC) is robust to heavy-tailed non-Gaussian noise. However, the approximate linear measurement equation obtained by statistical linearization technique may be not accurate enough since it only uses prior information. In this paper, a robust stable iterative maximum correntropy criterion UKF (RS-IMCC-UKF) is proposed by using nonlinear measurement function directly and numerical stability methods. Different from the existing UKF algorithms, we only need to perform one-time unscented transformation in each filtering cycle, reducing the execution time of algorithm. Then a nonlinear enhancement model is constructed to handle predictions and observations simultaneously, which will be included in the cost function of robust MCC. In the process of iterative solution, thanks to the latest iteration values which are used to update the measurement information, RS-IMCC-UKF has more accurate results compared with traditional filters. In order to avoid non-positive definite characteristic in covariance matrix and the inverse operation in ill-conditioned matrix, the hyperbolic QR decomposition and Moore–Penrose pseudo-inversion are introduced to improve the numerical stability of the algorithm. Finally, a target tracking is modeled to verify the effectiveness of the algorithm." @default.
• W4281567319 created "2022-05-27" @default.
• W4281567319 creator A5029952480 @default.
• W4281567319 creator A5034605279 @default.
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• W4281567319 date "2022-08-01" @default.
• W4281567319 modified "2023-10-16" @default.
• W4281567319 title "Robust stable iterated unscented Kalman filter based on maximum correntropy criterion" @default.
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• W4281567319 doi "https://doi.org/10.1016/j.automatica.2022.110410" @default.
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