Adaptive separation of independent sources: a deflation approach

In this paper, we address the adaptive blind source separation of independent sources using higher order statistics. Although this problem was considered in numerous works, none of the existing algorithms is guaranteed to converge to a relevant solution. Here, we propose a new separation scheme whose convergence is proved analytically. It is based on the observation that it is possible to extract one of the source signals by a simple algorithm obtained by extending to the source separation context some of the ideas developed by Shalvi-Weinstein in the framework of blind deconvolution. A low cost deflation procedure allows the extraction of the other source signals by means of the same algorithm. Simulation results illustrate the behaviour of this separation method