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A two-dimensional fast recursive least-squares algorithm is presented using a geometrical formulation based on the mathematical concepts of vector space, orthorgonal projection, and subspace decomposition. By appropriately ordering the 2-D data, the algorithm provides an exact least-squares solution to the deterministic normal equations. The method is further extended to the general FIR (finite impulse response) Wiener filter and the ARMA (autoregressive moving-average) modeling. The size and shape of the support region for both the MA and AR coefficients of the filter can be chosen arbitrarily.>
Sequeira et al. (Wed,) studied this question.