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Robust kernel recursive adaptive filtering algorithms based on M-estimate
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DOI：10.1016/j.sigpro.2023.108952.png)
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Abstract
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When coping with the large outliers in measurement caused by the non-Gaussian environmental noise, although the MCC criterion adopts the high-order statistics, the residual error for large outliers still ex-ists. Considering that the M-estimate works well in minimum square error criterion and it can trun-cate the outliers and further improve the robustness, in this paper, we propose the robust kernel recur-sive least squares algorithms and the robust kernel recursive maximum correntropy algorithms based on three M-estimate methods. Then, numerical simulations verify that the M-estimates help the proposed algorithms have better performance than the conventional kernel recursive adaptive filtering against the non-Gaussian noise.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Kernel adaptive filter (KAF)
M-estimate
Kernel recursive least squares (KRLS)
Kernel recursive maximum correntropy (KRMC)
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