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Journal of Electrical and Computer Engineering
Volume 2016, Article ID 2467198, 7 pages
Research Article

Adaptive Complex-Valued Independent Component Analysis Based on Second-Order Statistics

1College of Information and Communication Engineering, Harbin Engineering University, Heilongjiang 150001, China
2College of Eletrical and Information Engineering, Beihua University, Jilin 132012, China
3Collaborative Research Center, Meisi University, Tokyo 1918506, Japan

Received 1 April 2016; Revised 28 July 2016; Accepted 16 August 2016

Academic Editor: Panajotis Agathoklis

Copyright © 2016 Yanfei Jia and Xiaodong Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This paper proposes a two-stage fast convergence adaptive complex-valued independent component analysis based on second-order statistics of complex-valued source signals. The first stage constructs a cost function by extending the real-valued whiten cost function to a complex-valued domain and optimizes the cost function using a complex-valued gradient. The second stage uses the restriction that the pseudocovariance matrix of the separated signal is a diagonal matrix to construct the cost function and the geodesic method is used to optimize the cost function. Compared with other adaptive complex-valued independent component analysis, the proposed method shows a faster convergence rate and smaller error. Computer simulations were performed on synthesized signals and communications signals. The simulation results demonstrate the validity of the proposed algorithm.