Robust kernel recursive adaptive filtering algorithms based on M-estimate

被引：7

作者：

Yang, Xinyue ^{[1
]}

Mu, Yifan ^{[2
]}

Cao, Kui ^{[3
]}

Lv, Mengzhuo ^{[3
]}

Peng, Bei ^{[3
]}

Zhang, Ying ^{[2
]}

Wang, Gang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Informat & Commun Engn, Chengdu 611731, Peoples R China

[2] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Mech & Elect Engn, Chengdu 611731, Peoples R China

来源：

SIGNAL PROCESSING | 2023年 / 207卷

关键词：

Kernel adaptive filter (KAF); M-estimate; Kernel recursive least squares (KRLS); Kernel recursive maximum correntropy (KRMC);

D O I：

10.1016/j.sigpro.2023.108952

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：9