An Adaptive ARMA Spectral Estimator. Part 1.
Abstract
In this paper, a novel procedure for generating an ARMA spectral model of a wide sense stationary time series is developed. The parameters of this model are selected so that they most closely fit a set of Yule-Walker equations which are estimated from a finite set of time series' observations. This ARMA modeling method has been found to exhibit a spectral estimation performance which is typically superior to such alternatives as the maximum entropy (AR) method, classical Fourier procedures (MA), and, the Box-Jenkins method (ARMA). One of the principal features of this spectral estimation method is the elegant algebraic structure of the linear system of equations which need to be solved when finding the ARMA model's parameters. This shift-invariant type structure gives rise to an adaptive algorithmic solution procedure whose computational efficiency is comparable to that achieved by recently developed fast AR algorithmic methods. The details of the adaptive ARMA modeling procedure will be covered in Part 2 of this paper. These dual characteristics of excellent estimation performance and real time adaptive implementation mark this method as being a primary spectral estimation tool. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1981
- Accession Number
- ADA101869
Entities
People
- James A. Cadzow
- Randolph L. Moses
Organizations
- Virginia Tech