Huang Xiao-bin, Liu Hai-tao, Wan Jian-wei, Hu De-wen. Blind Signal Extraction Based on Subspace over High Noise Source Background[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2037-2040.
Citation:
Huang Xiao-bin, Liu Hai-tao, Wan Jian-wei, Hu De-wen. Blind Signal Extraction Based on Subspace over
High Noise Source Background[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2037-2040.
Huang Xiao-bin, Liu Hai-tao, Wan Jian-wei, Hu De-wen. Blind Signal Extraction Based on Subspace over High Noise Source Background[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2037-2040.
Citation:
Huang Xiao-bin, Liu Hai-tao, Wan Jian-wei, Hu De-wen. Blind Signal Extraction Based on Subspace over
High Noise Source Background[J]. Journal of Electronics & Information Technology, 2006, 28(11): 2037-2040.
It is a difficult problem to denoise in the low SNR, recently, Emir et al present a novel ICA denoising method, this method has been successfully applied to the function optical imaging. But in the very low SNR circumstance, because of the covariance matrix of the observed signals being singularity, the ICA denoising method can not be used. In order to resolve this problem, a new SICA denoising method based on the signal subspace is presented in this paper. The simulations show that compared to the ICA denoising method and the traditional filtering denoising methods, the method can not only get rid of the noise, but can successfully separation the signals.
Emir E, Akgul B, Akin A, et al.. Wavelet denoising vs ICA denoising for functional optical imaging[A]. Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering[C]. Capril Island, Italy, 2003: 384-387.[2]Hyvinen A, Karhunen J, Oja E. Independent Component Analysis[M]. New York, Wiley, 2001: Chapter 6-8.[3]Bell A, Sejnowski T. An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation.1995, 7(6):1129-1159[4]Hyvinen A, Oja E. A fast fixed-point algorithm for independent component analysis[J].Neural Computation.1997, 9(7):1483-[5]Zibulevsky M, Pearlmutter A. Blind source separation by sparse decomposition[J].Neural Computation.2001, 13(4):863-882
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