A multi-user Kurtosis algorithm for blind source separation
Constantinos B. Papadias · 2002
In earlier work we presented a set of necessary and sufficient conditions for the blind separation of a number of independent identically distributed (i.i.d.) source signals that share the same distribution and are mutually independent. We present an algorithm for implementing the multi-user Kurtosis (MUK) constrained optimization criterion suggested by these conditions. The algorithm is derived directly from the MUK cost function via a stochastic-gradient update at each iteration, followed by a Gram-Schmidt orthogonalization to project onto the criterion's constraint. A convergence analysis of the derived algorithm reveals that it is globally convergent (in the absence of noise) to a desired setting that recovers all the input sources, up to an arbitrary phase rotation each.