Asymptotic performance of eigenstructure spectral analysis methods

This paper considers some asymptotic statistical properties of covarianee eigenstructure spectral analysis techniques. It is shown that when the signal model is of the appropriate form, and the observations are Gaussian, the signal parameter estimates, obtained by locating the nulls in the eigen-spectrum, are asymptotically zero mean normal random variables. Based on this observation, the paper then considers the formation of confidence regions for the signal parameters. The paper presents the general case of a multi-dimensional eigenstructure algorithm, which estimates one or more parameters of each signal in the observed data.