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Mathematical Problems in Engineering
Volume 2015, Article ID 439264, 13 pages
Research Article

A New Method of Blind Source Separation Using Single-Channel ICA Based on Higher-Order Statistics

1Department of Information Engineering, University of Electronic Science and Technology of China, Chengdu, China
2College of Urban Railway Transportation, Shanghai University of Engineering and Science, Shanghai, China

Received 1 April 2015; Accepted 22 July 2015

Academic Editor: Carla Roque

Copyright © 2015 Guangkuo Lu et al. 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.


Methods of utilizing independent component analysis (ICA) give little guidance about practical considerations for separating single-channel real-world data, in which most of them are nonlinear, nonstationary, and even chaotic in many fields. To solve this problem, a three-step method is provided in this paper. In the first step, the measured signal which is assumed to be piecewise higher order stationary time series is introduced and divided into a series of higher order stationary segments by applying a modified segmentation algorithm. Then the state space is reconstructed and the single-channel signal is transformed into a pseudo multiple input multiple output (MIMO) mode using a method of nonlinear analysis based on the high order statistics (HOS). In the last step, ICA is performed on the pseudo MIMO data to decompose the single channel recording into its underlying independent components (ICs) and the interested ICs are then extracted. Finally, the effectiveness and excellence of the higher order single-channel ICA (SCICA) method are validated with measured data throughout experiments. Also, the proposed method in this paper is proved to be more robust under different SNR and/or embedding dimension via explicit formulae and simulations.