Parameter estimation for linear multidimensional non-Gaussian signals

The author considers the problem of estimating the parameters of a general (possibly asymmetric noncausal and/or nonminimum phase) two-dimensional autoregressive moving average random field model driven by an independent and identically distributed two-dimensional non-Gaussian sequence. Several inverse filter criteria were recently considered for parameter estimation of the system parameters given only the output measurements (image pixels). These criteria were shown to yield strongly consistent parameter estimates. The focus is on the computational aspects. A computer simulation example is presented to illustrate the performance of the proposed approach