Robust adaptive filtering algorithm based on maximum correntropy criteria for censored regression
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(Published version)
Date
2019
Authors
Wang, W.
Zhao, H.
Dogancay, K.
Yu, Y.
Lu, L.
Zheng, Z.
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Journal article
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Signal Processing, 2019; 160:88-98
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Abstract
Censored observations and impulsive measurement noise are encountered in many practical applications of adaptive signal processing. Traditional adaptive filtering algorithms may fail to work in such cases. This paper proposes a robust adaptive filter algorithm predicated on maximum correntropy criteria (MCC) for censored regression. A detailed performance analysis in terms of mean and mean-square behaviour is provided. Simulations with Gaussian and non-Gaussian noise are presented to verify the theoretical results, and to demonstrate the superior performance of the proposed algorithm over existing algorithms.
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Copyright 2019 Elsevier B.V.
Access Condition Notes: Accepted manuscript available after 1 April 2021