強(qiáng)噪聲背景下基于子空間的盲信號(hào)提取
Blind Signal Extraction Based on Subspace over High Noise Source Background
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摘要: 低信噪比下的去噪一直是一個(gè)難題，最近Emir等人提出了Independent Component Analysis(ICA)去噪方法，該方法在光學(xué)功能成像中得到了成功應(yīng)用。但研究發(fā)現(xiàn)在極低信噪比下，由于觀測(cè)數(shù)據(jù)的樣本協(xié)方差矩陣具有奇異性，這使得ICA去噪算法中的白化處理步驟無(wú)法進(jìn)行。為解決這一問題，本文利用子空間的概念，在ICA去噪方法的基礎(chǔ)上提出了一種新的基于子空間的ICA(ICA based on signal Subspace, SICA)去噪方法。仿真表明該方法能在極低信噪比下有效去噪，同時(shí)與傳統(tǒng)的濾波去噪相比， SICA去噪方法在去噪的同時(shí)還能夠成功地將頻域重疊的信號(hào)正確分離。
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關(guān)鍵詞:
- 盲信號(hào)提取; 獨(dú)立成分分析; 子空間分解; 濾波
Abstract: 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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