ROBUST LEARNING ALGORITHM FOR BLIND SEPARATION OF SIGNALS

The authors present a novel, efficient, self-normalising, unsupervised adaptive learning algorithm for the on-line (realtime) separation of statistically independent unknown source signals from a linear mixture of them. In contrast to the known algorithms the new algorithm allows the separation (or extraction) of extremely badly scaled signals (i.e. some or even all of the source and/or sensor signals can be very weak). Moreover, the mixing matrix can be very iii-conditioned.