IMPROVED NON-FOURIER FREQUENCY ESTIMATION OF SAMPLED STATIONARY TIME-SERIES

A general multi-component cyclic descent least-squares model used in conjunction with a power-of-two fast Fourier transform to perform the harmonic analysis presents numerous advantages over their individual use. Through the appropriate choice of a window taper and a linear filter applied to the time series data, severe leakage is effectively reduced by decreasing the interference between peaks throughout the band. The taper and filter characteristics are dependent on the given application. The resulting assembled frequency components are updated iteratively by means of the least-squares model. A brief portion of this study is devoted to the development of the Fourier transform as a general spectral estimator. Particular attention is given to its limitations. Methods for improving frequency identification in the transform are covered. A brief development of the cyclic descent least-squares model is given as a means of reducing errors inherent in a Fourier analysis. A numerical example is presented depicting the method. © 1992, Taylor & Francis Group, LLC. All rights reserved.