On the rate of convergence of the ML spectral estimate for identification of sinusoids in noise

The practical aspects of the convergence properties of the family of maximum-likelihood (ML) estimators are investigated in the context of harmonic signal estimation. Specifically, the consequences of having only a finite number of correlation lags and of performing finite-resolution computations are addressed. The results of this investigation include guidelines for assessing the available frequency resolution and for improved estimates of signal power. Finally, an example is presented which demonstrates the advantages of using autoregressive and ML estimates jointly in harmonic signal estimation