Blind signal separation in noisy environments using a three-step quantizer
Independent component analysis in noisy channels needs special considerations, since standard solutions lead to a bias in the estimate of the parameters. We show three different approaches to mitigate the effects of additive noise in the transfer medium. A principal component subspace method can reduce the noise to more favorable levels, so that any following algorithm shows reduced bias effects. Although stochastic-gradient algorithms for maximum-likelihood solutions to the problem can easily be found, they are computationally prohibitive. A very successful approach is, therefore, to assume zero noise power for the derivation of the adaptive algorithm and subsequently trying to compensate for any bias introduced by such a solution. The threshold nonlinearity (three-step quantizer) is suitable for the blind separation of a large class of sub-Gaussian distributions. Stability regions are explored followed by algorithmic extensions to suppress the bias in the estimation of the separation matrix.
threshold nonlinearity unbiased blind signal separation noisy ica ml solution overdetermined blind separation