
Accession Number : ADA130954
Title : Two Dimensional Linear Prediction Models. Part 1. Spectral Factorization and Realization.
Descriptive Note : Technical rept.,
Corporate Author : CALIFORNIA UNIV DAVIS SIGNAL AND IMAGE PROCESSING LAB
Personal Author(s) : Ranganath,Surendra ; Jain,Anil K
PDF Url : ADA130954
Report Date : May 1983
Pagination or Media Count : 74
Abstract : This paper presents several results for three different canonical forms of linear prediction on a plane. These filters have causal, semicausal and noncausal prediction geometries. Starting from their properties the authors consider the problem of realization of these filters from a given power spectral density function (SDF). Since it is not possible in general to obtain rational spectral factors of a two dimensional SDF, they propose algorithms for obtaining rational approximations which are stable and converge to their limit (irrational) factors as the order of approximation is increased. It is also shown that the normal equations associated with the minimum variance twodimensional prediction filters give a useful algorithm for obtaining rational approximations which are stable and converge to their unique limit filters. This result allows design of finite order, stable filters by solving a finite number of equations while realizing the given SDF arbitrarily closely. (Author)
Descriptors : *Mathematical models, *Mathematical prediction, *Mathematical filters, Algorithms, Two dimensional, Linearity, Equations, Density, Spectra, Signal processing, Approximation(Mathematics), Stability, Convergence
Subject Categories : Theoretical Mathematics
Distribution Statement : APPROVED FOR PUBLIC RELEASE