Iterative methods for estimation of 2-D AR parameters using a data-adaptive Toeplitz approximation algorithm

A new two-dimensional data-adaptive algorithm utilizing the iterative Toeplitz approximation method is presented. This algorithm provides a robust and efficient means for accurate estimation of 2-D autoregressive parameters representing spatially variant data arrays. Its convergence performance is comparable to that of the 2-D Recursive Least Squares (RLS) algorithm but is computationally more efficient. The savings in computation is realized by reducing the size of the matrix to be inverted when solving the AR model normal equations. The ability of the algorithm to accurately estimate the model parameters using very small data sets and various windowing schemes are evaluated. Spectral estimates are compared for quarter-plane (QP), nonsymmetric half-plane (NSHP) and combined-quadrant (CQ) regions of support. Additionally, the algorithm is tested in noise cancellation and line enhancement applications using image arrays. This algorithm may be implemented for data-adaptive image processing or coding and for applications requiring 2-D spectral estimation.http://archive.org/details/iterativemethods00eremLieutenant, U.S. NavyApproved for public release; distribution is unlimited