COMPLEX DOMAIN FLEXIBLE NON-LINEAR FUNCTION FOR BLIND SIGNAL SEPARATION

In this paper a new adaptive non-linear function for blind complex domain signal processing is presented. It is based on a couple of spline functions, one for the real and one for the imaginary part of the input, whose control points are adaptively changed using gradient-based techniques. B-splines are used, because they allow to impose only simple constraints on the control parameters in order to ensure a monotonously increasing characteristic. This new adaptive function is then applied to the outputs of a one-layer neural network in order to separate complex signals from mixtures by maximizing the entropy of the function outputs. We derive a simple form of the adaptation algorithm and present some experimental results that demonstrate the effectiveness of the proposed method.