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- W2543046598 abstract "An algorithm is presented for underdetermined blind source separation, i.e., the number of observed signals is less than that of original sources. Traditional solutions based on minimizing the L <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> -norm have some disadvantages in searching the optimal sub-matrix for separation. In the proposed algorithm, first we use a potential function to estimate the mixing matrix by clustering method. Then we present an improved L <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> -norm algorithm by weighting the observed signals vectors at the different source clustering directions. This method makes good use of the super-Gaussian property of sources and overcomes the disadvantages of L <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> -norm-based solutions. Furthermore, the case of an arbitrary mixing matrix is discussed in this paper. Simulation results have shown that the proposed approach can give better separation results than traditional methods in terms of signal-to-noise ratio." @default.
- W2543046598 created "2016-11-04" @default.
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- W2543046598 date "2007-11-01" @default.
- W2543046598 modified "2023-09-25" @default.
- W2543046598 title "An Improved L<sup>1</sup>-Norm Algorithm for Underdetermined Blind Source Separation Using Sparse Representation" @default.
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- W2543046598 doi "https://doi.org/10.1109/acssc.2007.4487155" @default.
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