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W2947555983 abstract "This technical note is aimed to derive the Chandrasekhar-type recursion for the maximum correntropy criterion (MCC) Kalman filtering (KF). For the classical KF, the first Chandrasekhar difference equation was proposed at the beginning of 1970s. This is the alternative to the traditionally used Riccati recursion and it yields the so-called fast implementations known as the Morf-Sidhu-Kailath-Sayed KF algorithms. They are proved to be computationally cheap because of propagating the matrices of a smaller size than n × n error covariance matrix in the Riccati recursion. The problem of deriving the Chandrasekhar-type recursion within the MCC estimation methodology has never been raised yet in engineering literature. In this technical note, we do the first step and derive the Chandrasekhar MCC-KF estimators for the case of adaptive kernel size selection strategy, which implies a constant scalar adjusting weight. Numerical examples substantiate a practical feasibility of the newly suggested MCC-KF implementations and correctness of the presented theoretical derivations." @default.
W2947555983 created "2019-06-07" @default.
W2947555983 creator A5041363585 @default.
W2947555983 date "2020-02-01" @default.
W2947555983 modified "2023-09-24" @default.
W2947555983 title "Chandrasekhar-Based Maximum Correntropy Kalman Filtering With the Adaptive Kernel Size Selection" @default.
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W2947555983 doi "https://doi.org/10.1109/tac.2019.2919341" @default.
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