Two-dimensional spectral estimation using spatial autoregressive models

Two-dimensional spectral estimation from raw data is of interest in signal and image processing. In this paper, a parametric technique using non causal spatial autoregressive models for spectral estimation is given. The spatial autoregressive models characterize the statistical dependency of the observation at location s on its neighbors in all directions. Once an appropriate model is fitted, the spectrum is a function of the model parameters. By assuming specific boundary conditions maximum likelihood estimates of model parameters are obtained. The usefulness of the method developed here is illustrated by resolving two closely spaced sinusoids on the plane.