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• W2037693075 abstract "This paper addresses the problem of recursive estimation of a process in presence of outliers among the observations. It focuses on deriving robust approximate Kalman-like backward filtering and backward-forward fixed-interval smoothing algorithms in the context of linear hidden Markov chain with heavy-tailed measurement noise. The proposed algorithms are derived based on the backward Markovianity of the model as well as the variational Bayesian approach. In a simulation design, our algorithms are shown to outperform the classical Kalman filter in the presence of outliers." @default.
• W2037693075 created "2016-06-24" @default.
• W2037693075 creator A5059020421 @default.
• W2037693075 creator A5059969352 @default.
• W2037693075 date "2013-05-01" @default.
• W2037693075 modified "2023-09-25" @default.
• W2037693075 title "Backward hidden Markov chain for outlier-robust filtering and fixed-interval smoothing" @default.
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• W2037693075 doi "https://doi.org/10.1109/icassp.2013.6638716" @default.
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