Weighted closed-form estimators for blind source separation
Vicente Zarzoso, F. Herrmann, Asoke Kumar Nandi · 2002
This paper investigates a novel closed-form estimation class, so-called weighted estimator (WE), for blind source separation in the basic two-signal problem. The proper combination of previously proposed estimators yields consistent estimates of the separation parameters under general conditions. In the real-mixture case, we determine analytic expressions for the WE asymptotic (large-sample) variance and the source-dependent weight value of the most efficient estimator in the class. By means of the bicomplex-number formalism, the WE is extended to the complex-mixture scenario, for which Cramer-Rao bounds are also derived. Simulations compare the WE with other methods, demonstrating its potential.