Gradient Algorithms for Complex Non-Gaussian Independent Component/Vector Extraction
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We address the problem of extracting one independent component from an instantaneous linear mixture of signals. Compared to Independent Component Analysis, a novel parameterization of the mixing model is used. Our statistical model is based on the non-Gaussianity of the source of interest, while the other background signals are assumed to be Gaussian. Three gradient-based estimation algorithms are derived using the maximum likelihood principle. These ideas and algorithms are also generalized for the extraction of a vector component when the extraction proceeds jointly from a set of instantaneous mixtures. In simulations, we mainly focus on the size of the region of convergence for which the algorithms guarantee the extraction of the desired source. The proposed methods show superior results under various levels of initial signal-to-interference ratio, in comparison with state-of-the-art algorithms. The computational complexity of the proposed algorithms grows linearly with the number of channels.
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