Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models

In this paper, four batches least squares linear approaches are developed for non-minimum phase bidimensional non-Gaussian moving average (MA) models identification. A relationship between autocorrelation and cumulant sequences is established. One of the proposed methods is cumulant based. The others exploit both autocorrelation and m  th-order cumulants (m>2m>2). Three of these proposed methods are obtained by transforming Brillinger–Rosenblatt's non-linear equation into linear one using the Tugnait's closed-form solution. We also generalize the 2-D version of Giannakis–Mendel's method to mth-order cumulant. The simulation results show that one of the three autocorrelation and cumulants based methods gives the best estimates in free-noise environments, but in a Gaussian noisy case, the cumulant-based one is more adequate when large data are available. We also show the usefulness of the relationship to improve the estimates of the autocorrelation-based method in colored noise environment.