A model based approach for 2-D MEPS analysis

A stochastic model based approach for 2-D MEPS analysis of a given finite uniform array data is presented. The method consists of fitting an appropriate 2-D noncausal Gaussian Markov Random Field (GMRF) model to the given data using maximum likelihood (ML) method for parameter estimation. The nonlinear criterion function used for ML estimation is similar in structure to the function arising in the algebraic approach of Lang and McClellan [1]. An example is given to illustrate the usefulness of our approach.