Autoregressive and Maximum Likelihood Spectral Analysis Methods.

摘要

Three power spectral density estimation problems are considered in this presentation. These are the estimation of power spectral density for a single scalar time series (or equivalently a line array), estimation of power spectral density matrices for vector valued time series, and the estimation of wavenumber or frequency-wavenumber spectra for random fields. The paper is primarily a tutorial coverage of autoregressive (also called maximum entropy) and maximum likelihood methods of spectral analysis. In addition to standard material on these subjects, a recent generalization of the Burg algorith, from scalar to vector time series is included. (Author)