Fully Orthogonal 2-D Lattice Structures for Quarter-Plane and Asymmetric Half-Plane Autoregressive Modeling of Random Fields

This paper is mainly devoted to the derivation of a new fully orthogonal two-dimensional (2-D) lattice structure for general autoregressive (AR) modeling of random fields. Similar to the 1-D lattice theory, this approach is based on recursive incrementation of the prediction support region by adding a single past observation point at each stage. In addition to developing the basic theory, the presentation includes horizontal and vertical building blocks of the proposed causal 2-D AR lattice filters. The algorithm presented here is useful for high-resolution 2-D spectral analysis applications. It is shown that the new fully orthogonal 2-D lattice structure can be an efficient tool for high-resolution radar imaging.