BLIND SEPARATION OF INSTANTANEOUS MIXTURE OF SOURCES VIA AN INDEPENDENT COMPONENT ANALYSIS

In this paper, we introduce a procedure for separating a multivariate distribution into nearly independent components based on minimizing a criterion defined in terms of the Kullback-Leibner distance. By replac ing the unknown density with a kernel estimate, we derive useful forms of this criterion when only a sample from that distribution is availa ble. We also compute the gradient and Hessian of our criteria for use in an iterative minimization. Setting this gradient to zero yields a s et of separating functions similar to the ones considered in the sourc e separation problem except that here, these functions are adapted to the observed data. Finally, some simulations are given, illustrating t he good performance of the method.