.png){kind=icon}
返回
Robust kernel recursive adaptive filtering algorithms based on M-estimate
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2023.108952.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Kernel adaptive filter (KAF)
M-estimate
Kernel recursive least squares (KRLS)
Kernel recursive maximum correntropy (KRMC)
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏分享收藏
分享收藏Adaptive Detection With Constant False Alarm Ratio in a Non-Gaussian Noise Background在非高斯噪声背景下具有恒定虚警率的自适应检测
PREAI
分享收藏
查看更多内容