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Adaptive blind separation of underdetermined mixtures based on sparse component analysis

Science in China(Series F) 2008年第4期51卷 381-393页

作者： YANG ZuYuan HE ZhaoShui XIE ShengLi FU YuLi School of Electronics & Information Engineering South China University of Technology Guangzhou 510640 China

The independence priori is very often used in the conventional blind source separation （BSS）. Naturally, independent component analysis （ICA） is also employed to perform BSS very often. However, ICA is difficult to use in some challenging cases, such as underdetermined BSS or blind separation of dependent sources. Recently, sparse component analysis （SCA） has attained much attention because it is theoretically available for underdetermined BSS and even for blind dependent source separation sometimes. However, SCA has not been developed very sufficiently. Up to now, there are only few existing algorithms and they are also not perfect as well in practice. For example, although Lewicki-Sejnowski＇s natural gradient for SCA is superior to K-mean clustering, it is just an approximation without rigorously theoretical basis. To overcome these problems, a new natural gradient formula is proposed in this paper. This formula is derived directly from the cost function of SCA through matrix theory. Mathematically, it is more rigorous. In addition, a new and robust adaptive BSS algorithm is developed based on the new natural gradient. Simulations illustrate that this natural gradient formula is more robust and reliable than Lewicki-Sejnowski＇s gradient.

关键词： underdetermined mixtures blind source separation (BSS) dependent sources sparse component analysis (SCA) sparse representation independent component analysis (ICA) natural gradient

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A Fixed Point Algorithm for Blind Identification of underdetermined mixtures

A Fixed Point Algorithm for Blind Identification of Underdet...

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2010 Chinese Control and Decision Conference

作者： Mingjian Zhang School of Automation,Guangdong University of Technology,Guangzhou,510006

In this paper,we consider the problem of blind identification in the underdetermined instantaneous mixtures case,where there are more sources than ***,a simultaneous matrix diagonalization(SMD)-based method has been proposed without any need of sparsity assumption on *** of using SMD technique,we propose a fixed point algorithm,which estimates the columns of the mixing matrix sequentially one by *** simulations are presented in order to demonstrate that the proposed algorithm has the ability to identify the mixing matrix,and its performance is comparable to that of SMD-based algorithm.

关键词： Blind identification fixed point algorithm underdetermined mixtures parallel factor(PARAFAC) analysis simultaneous matrix diagonalization(SMD)

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