Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6958207 | Signal Processing | 2016 | 15 Pages |
Abstract
In this brief, a robust and sparse recursive adaptive filtering algorithm, called convex regularized recursive maximum correntropy (CR-RMC), is derived by adding a general convex regularization penalty term to the maximum correntropy criterion (MCC). An approximate expression for automatically selecting the regularization parameter is also introduced. Simulation results show that the CR-RMC can significantly outperform the original recursive maximum correntropy (RMC) algorithm especially when the underlying system is very sparse. Compared with the convex regularized recursive least squares (CR-RLS) algorithm, the new algorithm also shows strong robustness against impulsive noise. The CR-RMC also performs much better than other LMS-type sparse adaptive filtering algorithms based on MCC.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Xie Zhang, Kaixin Li, Zongze Wu, Yuli Fu, Haiquan Zhao, Badong Chen,