High resolution model based 2-D spectrum estimation

Abstract

A noncausal autoregressive (NCAR) plus additive noise model is presented for model-based spectrum estimation of two-dimensional sinusoidal signals in noise. The maximum-likelihood (ML) procedure provides consistent and efficient parameter estimates for NCAR models with bilateral neighbor sets, and these properties carry over to the maximum-likelihood estimates of parameters for Gaussian-NCAR-plus-noise models. By assuming a toroidal lattice the complexity of the ML equation is significantly reduced with little impact on the observed accuracy of the estimated spectra. Initial conditions for starting the ML computation are proposed. Experimental results are presented for various signal-to-noise ratios.<>