Advances in Lee–Schetzen Method for Volterra Filter Identification

Abstract

This paper concerns the identification of nonlinear discrete causal systems that can be approximated with the Wiener--Volterra series. Some advances in the efficient use of Lee--Schetzen (L--S) method are presented, which make practical the estimate of long memory and high order models. Major problems in L--S method occur in the identification of diagonal kernel elements. Two approaches have been considered: approximation of gridded data, with interpolation or smoothing, and improved techniques for diagonal elements estimation. A comparison of diagonal elements estimated, with different methods has been shown with extended tests on fifth order Volterra systems.