Gradient-based blind deconvolutions with flexible approximated Bayesian estimator

In this paper a new blind deconvolution algorithm as modification of the Bellini's (1986) "Bussgang" is presented. First, a novel version based on stochastic gradient steepest descent error minimization technique is proposed. Then the Bayesian estimator used by Bellini is approximated with a flexible "sigmoid" parametrized with adjustable amplitude and slope, and a gradient-based technique is proposed to adapt such parameters in order to avoid their unsuitable choices. Experimental results are shown to assess the usefulness of the new equalization method.