Blind separation of uniformly distributed signals: A general approach

A general algorithm for blind separation of uniformly distributed signals is presented. First maximum likelihood equations are obtained for dealing with this task. It is difficult to obtain a closed form maximum likelihood solution for arbitrary mixing matrix. The learning rules are obtained based on the geometric interpretation of the maximum likelihood estimator. The algorithm, under special constraint of orthogonal mixing matrix, is the same as the O(1/T²) convergent algorithm. Special noise correction mechanisms are incorporated in the algorithm, and it has been found that the algorithm exhibits stable performance even in the presence of large amount of noise.