Generalized Correntropy Induced Metric Memory-Improved Proportionate Affine Projection Sign Algorithm and Its Combination

被引：6

作者：

Li, Guoliang ^{[1
]}

Zhang, Hongbin ^{[1
]}

Zhao, Ji ^{[1
]}

机构：

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

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2020年 / 67卷 / 10期

基金：

中国国家自然科学基金;

关键词：

Signal processing algorithms; Convergence; Approximation algorithms; Steady-state; Shape; Kernel; Convex combination; impulsive noise; spare identification; generalized correntropy induced metric (GCIM); proportionate affine projection sign algorithm (P-APSA); CONVEX COMBINATION;

D O I：

10.1109/TCSII.2019.2945783

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：

In this brief, an efficient memory-improved proportionate affine projection sign algorithm with the generalized correntropy induced metric (GCI-M-IP-APSA) is proposed to modify the filtering performance for sparse system identification. In addition, a simplified implementation of GCI-M-IP-APSA is derived, and called as SGCI-M-IP-APSA, which has lower computational complexity and realizes comparable filtering performances with that of GCI-M-IP-APSA. However, the major limitation of proposed APSA-type algorithm is a poor convergence behavior in non-stationary situations. To overcome such issue, a convex combination method is applied to SGCI-M-IP-APSA to enhance the tracking performance. The good performances of proposed algorithms are verified in sparse system identifications with impulsive noise and abrupt changes.

引用

页码：2239 / 2243

页数：5

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