A FAST FIXED-POINT ALGORITHM FOR INDEPENDENT COMPONENT ANALYSIS 
OF COMPLEX VALUED SIGNALS

International Journal of Neural Systems, Vol. 10, No. 1 (February, 2000) 1-8

Ella Bingham and Aapo Hyvärinen
Neural Networks Research Centre
Helsinki University of Technology
P.O. Box 5400, FIN-02015 HUT, Finland

Ella.Bingham@hut.fi, Aapo.Hyvarinen@hut.fi
http://www.cis.hut.fi/projects/ica/

ABSTRACT:

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 fixed-point type algorithm that is 
capable of separating complex valued, linearly mixed source signals is 
presented and its computational efficiency is shown by simulations. 
Also, the local consistency of the estimator given by the algorithm is 
proved.