Principal component analysis of blind signal separation with nonlinear approaches
Jinping Ni · 2001
The independent source signals can be separated from their linear mixture by incorporating nonlinear function into a standard principal component analysis (PCA) which is a widely used technique in statistical signal processing. Some recent nonlinear PCA approaches to blind signal separation are reviewed in present paper. It is found that present nonlinear PCA is only valid to real valued signals but invalid to complex valued signal.With modifying the form of activation function, a class of complex nonlinear PCA algorithms which is successful to blind complex signals separation is proposed. Furthermore, the property for separating the mixture of sub-and super-Gaussian signals is analyzed and evaluated by computer simulation.