Sparse modeling of super-resolution near-field structure read-out signals

Abstract

In this study, super-RENS disc read-out signals were modeled with various nonlinear models. In the results of modeling the nonlinearity, DC offset, and domain bloom phenomena of the super-RENS channel with a second-order Volterra filter, it was identified that the number of significant quadratic coefficients was relatively small. Therefore, a sparse Volterra model and a truncated Volterra model are applied to effectively model the nonlinear channel. In addition, the Hammerstein-type asymmetric conversion model is employed to model the nonlinear channel for the purpose of comparison. The results demonstrate that the sparse model of a simplified polynomial form achieves good modeling performance for the super-RENS disc read-out signals.