Authors: Yina Guo; Shuhua Huang
Addresses: Department of Electronics and Information Engineering, Taiyuan University of Science and Technology, ShanXi Taiyuan, China ' Department of Electronics and Information Engineering, Taiyuan University of Science and Technology, ShanXi Taiyuan, China
Abstract: In commonly used independent component analysis (ICA)-based methods for blind separation of single input multiple outputs, such as single channel ICA, wavelet ICA and ensemble empirical mode decomposition (EEMD) ICA, the prior knowledge of the signals is assumed to be known, the sources are assumed to be disjointing in the frequency domain or the main channels selection from multi-channel outputs is not automatic. A new method based on EEMD, principal component analysis (PCA) and ICA that makes no such assumptions is presented in this paper. EEMD describes any time-domain signal as a finite set of oscillatory components called intrinsic mode functions (IMFs). PCA can reduce dimensions of IMFs by using orthogonal transformation. ICA finds the independent components by maximising the statistical independence of the dimensionality reduction IMFs. The separation performance of our algorithm is compared with EEMD-ICA through simulations. The experimental results show our method outperforms EEMD-ICA with lower relative root mean squared error (RRMSE) and higher cross-correlation.
Keywords: blind source separation; BSS; ensemble empirical mode decomposition; intrinsic mode functions; independent component analysis; ICA; principal component analysis; PCA; simulation.
International Journal of Computer Applications in Technology, 2013 Vol.47 No.2/3, pp.256 - 261
Available online: 05 Jun 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article