Blind deconvolution with minimum Renyi's entropy

Blind techniques attract the attention of many researchers due to their numerous promising applications in different fields of signal processing, from communications to control systems. Blind deconvolution is a problem that has been investigated in detail over the last two decades. Many approaches adopting various optimization criteria have been proposed to determine the inverse of the unknown channel filter. Minimum entropy deconvolution, as summarized by Donoho, provides an effective tool for determining the deconvolving filter using only the observed data. Recently, we have proposed an estimator for Renyi's entropy based on Parzen windowing, and demonstrated its superior performance over other entropy estimators in blind source separation and other problems. In this paper, we present a blind deconvolution algorithm based on the minimization of this entropy estimator and investigate its performance through Monte Carlo simulations.