Convergence behavior of two-dimensional least-squares lattice algorithm

In this paper, we propose a two — dimensional (2-D) least-squares lattice (LSL) algorithm for the general case of the auto regress i ve (AR.) model with an asymmetric half-plane (AHP) coefficient support. The resulting LSL algorithm gives both order and space recursions for the 2-D deterministic normal equation. The size and shape of the coefficient support region of the proposed lattice filter can be chosen arbitrarily. Although the 2-D signals of the model support are ordered into a one-dimensional (ID) array, the ordering of the support signal can be assigned arbitrarily. Finally, computer simulation for modeling a texture image is demonstrated to confirm the proposed model gives rapid convergence.