A fast xed-point algorithm for independent component analysis of complex valued signals
نویسنده
چکیده
Separation of complex valued signals is a frequently arising problem in signal processing. For example, separation of convolutively mixed source signals involves computations on complex valued signals. In this article it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved by the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector into components that are mutually as independent as possible. In this article, a fast xed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and its computational eeciency is shown by simulations. Also, the local consistency of the estimator given by the algorithm is proved.
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In this paper, we derive new xed-point algorithms for the blind separation of complex-valued mixtures of independent, possibly non-circularly-symmetric, and non-Gaussian source signals. Leveraging recent results in complex independent component analysis, we construct iterative procedures for complex signal mixtures whose evolutionary characteristics are identical to those of the real-valued Fa...
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