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.
Gradient-based blind deconvolutions with flexible approximated Bayesian estimator / Fiori, S; Uncini, Aurelio; Piazza, F.. - 2:(1998), pp. 854-858. [10.1109/IJCNN.1998.685879]
Gradient-based blind deconvolutions with flexible approximated Bayesian estimator
UNCINI, Aurelio;
1998
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.