Abstract

We propose an independent component analysis (ICA) algorithm which can separate mixtures of sub- and super- Gaussian source signals with self-adaptive nonlinearities. The ICA algorithem in the framework of natural Riemannian gradient is derived using the parameterized Weibull density model. The nonlinear function in ICA algorithem is self-adaptive and is controlled by the shape parameter of Weibull density model. Computer simulation results confirm the validity and high performance of the proposed algorithm

Key words: Independent component analysis, weibull distribution, maximum likelihood, sub- and super- Gaussian, blind signal separation.