Deconvolution and Equalization of Nonminimum-Phase Systems with only NonGaussian Measurements

• Main Authors: Wu-Ton Chen, 陳梧桐
• Other Authors: Prof. Chong-Yung Chi
• Format: Others
• Language: en_US
• Published: 1994
• Online Access: http://ndltd.ncl.edu.tw/handle/90787941909620290232

博士 === 國立清華大學 === 電機工程研究所 === 82 === Deconvolution is a signal processing procedure for removing the effects of a linear signal distorting system to estimate the desired input signal with only output measurements. The deconvolution problem can be found in many signal processing areas such as digital communications, seismic data processing, speech analysis, image processing, spectroscopy and ultrasonic nondestructive evaluation. The conventional predictive deconvolution assumes that the desired signal is a white process. Generally speaking, the more accurate the model used for the desired input signal, the better the performance of the developed deconvolution algorithm is. For instance, Kormylo and Mendel used a zero-mean white Bernoulli-Gaussian (B-G) model for the reflectivity sequence in seismology which is a non-Gaussian sparse spike train with both positive and negative amplitudes. Quite many B-G model based deconvolution algorithms were reported in the past 15 years, which provide deconvolution results with much higher resolution than the predictive deconvolution does at more computation expense. A common characteristic of the existing B-G model based deconvolution algorithms is that they can estimate the desired input signal with high-resolution and recover the phase of the (minimum or nonminimum phase) linear signal distorting system.