Two-dimensional orthogonal lattice structures for autoregressive modeling of random fields
Two-dimensional orthogonal lattice structures for autoregressive modeling of random fields
复制标题
用于随机场自回归建模的二维正交晶格结构
DOI：
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发表时间：
1996
影响因子：
5.4
通讯作者：
A. H. Kayran
中科院分区：
文献类型：
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作者：
A. H. Kayran
二维正交格型滤波器是一维格型参数理论的自然扩展。该方法提供了一个完整的解决方案的Levinson型算法计算预测误差滤波器系数使用格参数从给定的2-D增广正规方程。该理论可用于四分之一平面和非对称半平面模型。根据预测区域中的索引方案，示出了最终阶后向预测误差可以对应于不同的四分之一平面模型。除了发展的基本理论，文章包括这个晶格模型的几个性质。给出了格点模型稳定的条件和分解二维相关矩阵的有效方法。它表明，无限的前向和后向预测误差形成正交基。本文提出了一种简化二维正交格型滤波器的方法。所提出的2-D晶格方法与其他替代结构的概念背景和复杂性进行了比较。例子被认为是给定的协方差的情况下。
Two-dimensional orthogonal lattice filters are developed as a natural extension of the 1-D lattice parameter theory. The method offers a complete solution for the Levinson-type algorithm to compute the prediction error filter coefficients using lattice parameters from the given 2-D augmented normal equations. The proposed theory can be used for the quarter-plane and asymmetric half-plane models. Depending on the indexing scheme in the prediction region, it is shown that the final order backward prediction error may correspond to different quarter-plane models. In addition to developing the basic theory, the article includes several properties of this lattice model. Conditions for lattice model stability and an efficient method for factoring the 2-D correlation matrix are given. It is shown that the unended forward and backward prediction errors form orthogonal bases. A simple procedure for reduced complexity 2-D orthogonal lattice filters is presented. The proposed 2-D lattice method is compared with other alternative structures both in terms of conceptual background and complexity. Examples are considered for the given covariance case.