The algebraic inversion of 2-D autoregressive power spectra with applications to spectral estimation

Some recent results concerning the evaluation of autocorrelation functions associated with 2-D autoregressive (AR) spectra are reviewed. In contrast to the 1-D case, 2-D AR autocorrelation functions can, in general, only be evaluated by means of a numerical integration. However, if the minimum-phase whitening filter for the AR process has finite reflection coefficient support, then the autocorrelation function can be evaluated algebraically, either by means of a "backward" 2-D Levinson algorithm, or by means of a partial fraction expansion. The special properties of 2-D AR spectra of this class make them potentially useful in the problem of finding correlation-matching spectral estimates. The possibility of performing 2-D covariance extension by fitting AR models with finite reflection coefficient Support iS partially explored.