Abstract
This paper presents an extension of the one-dimensional (1-D) lattice method of linear prediction parameter estimation, first popularized by Burg, to the two-dimensional (2-D) image data application. A fast computational algorithm for the solution of the constrained least squares normal equations of the 2-D lattice method is provided. This algorithm is a significant computational simplification over and an estimation improvement on three previous attempts to extend the 1-D Burg lattice-based algorithm to 2-D. The fast algorithm exploits some special matrix structures missed by previous attempts. The technique presented here is useful for high-resolution 2-D spectral analysis applications [e.g., high-resolution synthetic aperture radar (SAR) imagery from spotlight mode SAR systems].
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