Complex-valued ICA using second order statistics

In this paper we propose a novel transformation called strong-uncorrelating transform (SUT) that can be viewed as an extension of the conventional whitening transform for complex random vectors. The SUT is a second order technique that can be used as a fast independent component analysis (ICA) method for complex signals. It is able to separate almost all mixtures, if the sources belong to a class of complex non-circular random variables. Moreover, it can be used as a preprocessing technique in any complex ICA algorithm using whitening. If the sources are circular, the SUT reduces to the usual whitening transform. In other cases it reduces the computational load. Some relevant properties of the transform as well as some simulations examples are presented