Alternative Criteria for Predictive Blind Deconvolution
Renan Brotto, Kenji Nose-Filho, João Marcos Travassos Romano · Journal of Communication and Information Systems · 2018
Blind deconvolution is a major theme in signal processing and has been intensely investigated over the last decades. Among its several applications, we can mention the problem of seismic deconvolution and channel equalization in telecommunications. In these two cases, predictive techniques have been studied by different authors, and presented satisfactory results when some suitables conditions were fulfilled. In fact, the predictive deconvolution structure, when associated with the classical mean squared error criterion is only effective when the distortion system is minimum phase. In the case of nonminimum phase systems, it only provides magnitude equalization, but the phase response remains distorted. In order to overcome this problem, we present in this work some interesting results obtained with the use of Lp norms, with p different of 2, as optimization criteria. First we demonstrate that the Lp prediction error filter works as the Maximum Likelihood solution for blind deconvolution when the signal to be recovered has a generalized Gaussian distribution, with i.i.d (identically and independently distributed) samples. From this, we show how the best p can be chosen according to the signal distribution. Then we further investigate the phase response of the Lp filter, emphasizing its potential as well as some limitations in dealing with blind deconvolution, even for nonminimum phase distortion system. Finally, some performance simulations results are provided.