Model order estimation of 2D autoregressive processes

The work on model order estimation by Bayesian predictive densities of 1-D real autoregressive processes is extended to 2-D complex autoregressive processes. According to the procedure, the best model is the one which most accurately predicts the data yet to be observed and whose parameters are estimated from the data already observed. The derivation steps of the algorithm are demonstrated and verified by computer simulations. The computer simulations show that the algorithm based on this approach yields good results