Cyclic maximization of non-Gaussianity for blind signal extraction of complex-valued sources

被引：16

作者：

Duran-Diaz, Ivan ^{[1
]}

Cruces, Sergio ^{[1
]}

Auxiliadora Sarmiento-Vega, Maria ^{[1
]}

Aguilera-Bonet, Pablo ^{[1
]}

机构：

[1] Univ Seville, Escuela Tecn Super Ingenieros, Dept Teor Senal & Comunicac, Seville 41092, Spain

来源：

NEUROCOMPUTING | 2011年 / 74卷 / 17期

关键词：

Independent component analysis; Blind signal extraction; Negentropy criterion; SELF-RECOVERING EQUALIZATION; DS-CDMA; ICA;

D O I：

10.1016/j.neucom.2011.03.031

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article presents a new algorithm for the blind extraction of communications sources (complex-valued sources) through the maximization of negentropy approximations based on nonlinearities. A criterion based on the square modulus of a nonlinearity of the output is used. We decouple the arguments of the criterion so that the algorithm maximizes it cyclically with respect to each argument by means of the Cauchy-Schwarz inequality. A proof of the ascent of the objective function after each iteration is also provided. Numerical simulations corroborate the good performance of the proposed algorithm in comparison with the existing methods. (C) 2011 Elsevier B.V. All rights reserved.

引用

页码：2867 / 2873

页数：7