Two Dimensional Linear Prediction Models. Part 1. Spectral Factorization and Realization.
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 two-dimensional 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)
Document Details
- Document Type
- Technical Report
- Publication Date
- May 01, 1983
- Accession Number
- ADA130954
Entities
People
- Anil K. Jain
- Surendra Ranganath
Organizations
- University of California, Davis