Experimental comparison of fourier transformation and model-fitting methods of spectral analysis

Abstract

A summary of the results of an extensive comparative experimental study of Fourier transformation and model-fitting methods of spectral analysis of random time-series data is presented. It is illustrated that Fourier transformation methods can be an essential companion to model-fitting methods even for short data segments with underlying sharp spectral peaks. The best spectrum estimates can be obtained by taking advantage of the strengths of both types of methods. For example, it is shown that detection and estimation of the frequencies of spectral lines for short data segments can be best accomplished using certain parametric methods in conjunction with Fourier transformation methods to aid in model-order selection and identification of spurious peaks in the parametric spectrum estimate, and that estimation of amplitude and phase for sine-wave removal, given frequency estimates, and spectrum estimation after sine-wave removal can often be best accomplished with Fourier transformation methods alone.