ICA of Complex Valued Signals: A Fast and Robust Deflationary Algorithm

Authors:
Aapo Hyvärinen
Affiliations:
-
Venue:
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Year:
2000

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Abstract

Separation of complex valued signals is a frequently arising problem in signal processing. In this article, it is assumed that the original, complex valued source signals are mutually statistically independent, and the problem is solved b y the independent component analysis (ICA) model. ICA is a statistical method for transforming an observed multidimensional random vector in to components that are mutually as independent as possible. In this article, a fast 1/2xed-point type algorithm that is capable of separating complex valued, linearly mixed source signals is presented and simulations show its computational efficiency. We also present a theorem on the local consistency of the estimator given b y the algorithm.