Recursive Complex Blind Source Separation via Eigendecomposition of Cumulant Matrices
ICASSP (2)(2007)
摘要
Under the assumptions of non-Gaussian, non-stationary, or non-white independent sources, linear blind source separation can be formulated as a generalized eigenvalue decomposition problem. Here we provide an elegant method of doing this online, instead of waiting for a sufficiently large batch of data. This is done through a recursive generalized eigendecomposition algorithm that tracks the optimal solution, which is obtained using all the data observed. The algorithms proposed in this paper follow the well-known recursive least squares (RLS) algorithm in nature.
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关键词
recursive complex blind source separation,independent component analysis,higher order statistics,least squares approximations,blind source separation,matrix decomposition,linear blind source separation,nongaussian nonstationary nonwhite independent sources,cumulant matrix eigendecomposition,recursive estimation,recursive least squares algorithm,eigenvalues and eigenfunctions,generalized eigendecomposition,cumulants,recursive generalized eigendecomposition algorithm,eigenvalue decomposition,covariance matrix,decorrelation,least squares approximation,cumulant
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