Fixed-point ICA algorithm for blind separation of complex mixtures containing both circular and noncircular sources

作     者：Yao Junliang Ren Haipeng Liu Qing 

作者机构：School of Automation and Information Engineering Xi'an University of Technology 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2016年第23卷第2期

页      面：15-23页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 

基　　金：supported by the National Natural Science Foundation of China (61401354, 61172070) the Innovative Research Team of Shaanxi Province (2013KCT-04) 

主　　题：ICA fixed point iteration noncircular complex signal phase ambiguity 

摘      要：Fixed-point algorithms are widely used for independent component analysis（ICA） owing to its good convergence. However, most existing complex fixed-point ICA algorithms are limited to the case of circular sources and result in phase ambiguity, that restrict the practical applications of ICA. To solve these problems, this paper proposes a two-stage fixed-point ICA（TS-FPICA） algorithm which considers complex signal model. In this algorithm, the complex signal model is converted into a new real signal model by utilizing the circular coefficients contained in the pseudo-covariance matrix. The algorithm is thus valid to noncircular sources. Moreover, the ICA problem under the new model is formulated as a constrained optimization problem, and the real fixed-point iteration is employed to solve it. In this way, the phase ambiguity resulted by the complex ICA is avoided. The computational complexity and convergence property of TS-FPICA are both analyzed theoretically. Simulation results show that the proposed algorithm has better separation performance and without phase ambiguity in separated signals compared with other algorithms. TS-FPICA convergences nearly fast as the other fixed-point algorithms, but far faster than the joint diagonalization method, e.g. joint approximate diagonalization of eigenmatrices（JADE）.