Adaptive deconvolution and system identification using higher order moments

Abstract

Introduces a new method to adaptively deconvolve a linear process. The problem is to obtain the unknown linear system and the underlying white-noise process in a simple adaptive manner. The solution is based on second and higher order moments, and is exceedingly easy to implement. The method is radically different from the familiar gradient-based schemes used in adaptive filtering.<>