Gaussian Process Regression for Generalized Frequency Response Function Estimation

TL;DR

This paper explores Gaussian process regression techniques for estimating generalized frequency response functions (GFRFs), extending kernel-based system identification methods from time to frequency domain for improved accuracy.

Contribution

It introduces a novel application of Gaussian process regression to GFRF estimation, enhancing the accuracy and robustness of frequency domain system identification.

Findings

Improved GFRF estimation accuracy

Lower variance in frequency response estimates

Effective kernel-based modeling in frequency domain

Abstract

Kernel-based modeling of dynamic systems has garnered a significant amount of attention in the system identification literature since its introduction to the field. While the method was originally applied to linear impulse response estimation in the time domain, the concepts have since been extended to the frequency domain for estimation of frequency response functions (FRFs), as well as to the estimation of the Volterra series in time domain. In the latter case, smoothness and exponential decay was imposed along the hypersurfaces of the multidimensional impulse responses, allowing lower variance estimates than could be obtained in a simple least squares framework. The Volterra series can also be expressed in a frequency domain context, however there are several competing representations which all possess some unique advantages. Perhaps the most natural representation is the generalized frequency response function (GFRF), which is defined as the multidimensional Fourier transform of the corresponding Volterra kernel in the time-domain series. The representation leads to a series of frequency domain functions with increasing dimension.