A comparison of spectral estimators for real data

Spectral estimation of real data can be performed by a number of algorithms. This paper compares four methods of estimation. The comparisons are based on three examples which are evaluated in terms of the quality of the estimate, the complexity of the algorithm, and the noise immunity of the estimate. The four estimators are the well-known periodogram, Burg\´s maximum entropy (AR modelling) method, and two autoregressive-moving average (ARMA) models that have been developed recently here at the University of Colorado [1,2]. The examples chosen contain a smooth spectrum, a spectrum with "high peaks" and "deep valleys", and two sinusoids in white noise. Our results indicate that the ARMA methods are superior in a majority of cases.