Blind Signal Extraction Based on Subspace over High Noise Source Background
强噪声背景下基于子空间的盲信号提取

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.