Fitting MA models to linear non-Gaussian random fields using higher order cumulants

A general (possibly nonminimum phase and/or asymmetric noncausal) two-dimensional (2-D) moving average (MA) model driven by a zero-mean i.i.d. 2-D sequence is considered. The input sequence is not observed. The signal observations may be noisy. We consider the problems of model order determination and model parameter estimation using the higher order (third- or fourth-order, for example) cumulants of the 2-D signal. Second-order statistics of the data can consistently identify only a smaller class of MA models. The proposed approaches are illustrated via computer simulations