Blind separation of multiple binary sources using a single linear mixture
Konstantinos Diamantaras · 2002
Multiple binary sequences are blindly separated from a single linear mixture using the structure of the probability distribution function of the observed data. No specific assumptions are made regarding the second or higher order statistics of the sources. Assuming additive Gaussian noise the PDF is a mixture of Gaussians centered at points that uniquely determine (a) the mixing parameters and (b) the source signals up to a permutation and a sign ambiguity. We present both the theoretical framework for this novel approach and a new recursive blind separation algorithm based on our framework. Simulations show that the method can successfully separate at least up to 10 binary source signals at different noise levels.