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2011

Weight Adjusted Tensor Method for Blind Separation of Underdetermined Mixtures of Nonstationary Sources

7 years 10 months ago
Weight Adjusted Tensor Method for Blind Separation of Underdetermined Mixtures of Nonstationary Sources
—In this paper, a novel algorithm to blindly separate an instantaneous linear underdetermined mixture of nonstationary sources is proposed. It means that the number of sources exceeds the number of channels of the available data. The separation is based on the working assumption that the sources are piecewise stationary with a different variance in each block. It proceeds in two steps: 1) estimating the mixing matrix, and 2) computing the optimum beamformer in each block to maximize the signal-to-interference ratio of each separated signal with respect to the remaining signals. Estimating the mixing matrix is accomplished through a specialized tensor decomposition of the set of sample covariance matrices of the received mixture in each block. It utilizes optimum weighting, which allows statistically efficient (CRB attaining) estimation provided that the data obey the assumed Gaussian piecewise stationary model. In simulations, performance of the algorithm is successfully tested on b...
Petr Tichavský, Zbynek Koldovský
Added 15 May 2011
Updated 15 May 2011
Type Journal
Year 2011
Where TSP
Authors Petr Tichavský, Zbynek Koldovský
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