Blind sources separation using a rotation matrix identification algorithm
Liu Han, Ding Liu, Liu Xiaoyan · 2002
A new learning algorithm is developed for blind separation of independent source signals from their linear mixtures. In the noiseless real-mixture two-source two-sensor scenario, once the observations are whitened (decorrelated and normalized), only a Givens rotation matrix remains to be identified in order to achieve the source separation. In this paper, an adaptive estimator of the angle that characterizes such a rotation is derived. It shows that the estimator converges to a stable valid separation solution with the only condition that the sum of source kurtosis be distinct from zero. Simulation illustrates the validity of the algorithm.