Gradient-Based Blind Deconvolution with Flexible Approximated Bayesian Estimator

In this paper a new blind deconvolution algorithm as modzjkation of the Bellini ‘s ‘Bussgang ’ is presented. Firstly, a novel version based on stochastic Gradient Steep-est Descent error minimization technique isproposed. Then the Bayesian estimator used by Bellini is approximated with a flexible Sigmoid ’ parameterized with adjustable ampli-tude 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 use-fulness of the new equalization method