Identification of linear stochastic systems via second- and fourth-order cumulant matching
IEEE Transactions on Information Theory
Linear modeling of multidimensional non-Gaussian processes using cumulants
Multidimensional Systems and Signal Processing
Object and Texture Classification Using Higher Order Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fitting MA models to linear non-Gaussian random fields using higherorder cumulants
IEEE Transactions on Signal Processing
Parameter estimation of two-dimensional moving average randomfields
IEEE Transactions on Signal Processing
MA parameter estimation and cumulant enhancement
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Maximum likelihood parameter estimation of textures using a Wold-decomposition based model
IEEE Transactions on Image Processing
Adaptive restoration of textured images with mixed spectra
IEEE Transactions on Image Processing
Image modeling using inverse filtering criteria with application to textures
IEEE Transactions on Image Processing
Bispectral analysis and model validation of texture images
IEEE Transactions on Image Processing
Fast frequency template matching using higher order statistics
IEEE Transactions on Image Processing
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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 mth-order cumulants (m2). 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.