.png){kind=icon}
返回
Recursive constrained generalized maximum correntropy algorithms for adaptive filtering
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2022.108611.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Thanks to the ability of preventing the accumulation of errors, constrained adaptive filtering (CAF) algorithms have been widely applied. However, in practice, non-Gaussian noise may significantly degrade the filtering performance of CAFs derived from the second-order signal statistics. In this paper, we propose several constrained generalized maximum correntropy (CGMC) algorithms to overcome this problem, inspired by the robustness and flexibility of GMC to non-Gaussian noises. We first introduce a CGMC algorithm based on the gradient method. To improve its convergence rate with correlated inputs, we further propose a recursive CGMC (RCGMC) algorithm. For RCGMC, we conduct the convergence analysis, and characterize the theoretical transient mean square deviation (MSD) performance. Furthermore, we derive a low-complexity version of RCGMC by using the weighting method and the leading dichotomous coordinate descent (DCD) algorithm. Simulation results demonstrate the effectiveness of our proposed algorithms in non-Gaussian noise environment, and the consistency between the analytical and simulation results. (c) 2022 Elsevier B.V. All rights reserved.
Keyword:
Correntropy
Adaptive filtering
Linearly-constrained
Dichotomous coordinate descent
Non-Gaussian noise
Convergence analysis
期刊
IF:
3.6
论文数:
9.9K
被引数:
1.7W
机构
引用论文
On the Possible Connection between Photospheric 5-Min Oscillation and Solar Flare Microwave Emission
PREAI
分享收藏
Performance and Analysis of Recursive Constrained Least Lncosh Algorithm Under Impulsive Noises
PREAI
分享收藏Fixed-point generalized maximum correntropy: Convergence analysis and convex combination algorithms.png)
SIGNAL PROCESSING
SIGNAL PROCESSINGIF3.6
PREAI
分享收藏
查看更多内容