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...

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Veröffentlicht in:IEEE transactions on signal processing 2019-09, Vol.67 (17), p.4507-4520
Hauptverfasser: Kayran, Ahmet Hamdi, Camcioglu, Erdogan
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description 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.
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subjects 2-D lattice filters
2-D signal processing
2-D spectral estimation
Algorithms
autoregressive modeling
Autoregressive models
Autoregressive processes
Computational modeling
Fields (mathematics)
High resolution
Image resolution
Indexes
Lattice theory
Lattices
linear prediction
Mathematical model
Modelling
Prediction algorithms
Radar imaging
Recursive methods
Spectrum analysis
Two dimensional analysis
Two dimensional models
title Fully Orthogonal 2-D Lattice Structures for Quarter-Plane and Asymmetric Half-Plane Autoregressive Modeling of Random Fields
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