Complex Independent Component Analysis by Entropy Bound Minimization

摘要

We first present a new (differential) entropy estimator for complex random variables by approximating the entropy estimate using a numerically computed maximum entropy bound. The associated maximum entropy distributions belong to the class of weighted linear combinations and elliptical distributions, and together, they provide a rich array of bivariate distributions for density matching. Next, we introduce a new complex independent component analysis (ICA) algorithm, complex ICA by entropy-bound minimization (complex ICA-EBM), using this new entropy estimator and a line search optimization procedure. We present simulation results to demonstrate the superior separation performance and computational efficiency of complex ICA-EBM in separation of complex sources that come from a wide range of bivariate distributions.