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• W2086693977 startingPage "1555" @default.
• W2086693977 abstract "In this study, a discrete-time robust nonlinear filtering algorithm is proposed to deal with the contaminated Gaussian noise in the measurement, which is based on a robust modification of the derivative-free Kalman filter. By interpreting the Kalman type filter (KTF) as the recursive Bayesian approximation, the innovation is reformulated capitalizing on the Huber's M-estimation methodology. The proposed algorithm achieves not only the robustness of the M-estimation but also the accuracy and flexibility of the derivative-free Kalman filter for the nonlinear problems. The reliability and accuracy of the proposed algorithm are tested in the Univariate Nonstationary Growth Model." @default.
• W2086693977 created "2016-06-24" @default.
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• W2086693977 date "2013-11-01" @default.
• W2086693977 modified "2023-10-18" @default.
• W2086693977 title "Robust derivative-free Kalman filter based on Huber's M-estimation methodology" @default.
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• W2086693977 doi "https://doi.org/10.1016/j.jprocont.2013.05.004" @default.
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