Nonlinear spline adaptive filters based on a low rank approximation

Abstract

Nonlinear spline adaptive filters are a class of adaptive filters for modelling nonlinear systems. To improve the convergence performance of existing nonlinear spline adaptive filters (SAFs), in this paper, we propose a low rank approximation for different SAF models by incorporating the technique of nearest Kronecker product decomposition. We consider the Wiener and Hammerstein SAF models for developing the proposed algorithms, and simulation studies carried out show that improved convergence and tracking performance can be achieved compared to traditional SAFs.