Robust kernel recursive adaptive filtering algorithms based on M-estimate

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.