Adaptive modified covariance algorithms for spectral analysis
type: Item
instance of: scholarly article
title: Adaptive modified covariance algorithms for spectral analysis
zbMATH Open document ID: 0992.62089
DOI: 10.1016/S0165-1684(01)00178-5
author: Kyriakos Kitsios
author: Andreas Spanias
author: Bruno D. Welfert
published in: Signal Processing
publication date: 2002-07-15T00:00:00Z
review text: An optimum block modified covariance algorithm is developed for computing time-varying autoregressive (AR) parameters. The method presented here differs from those presented previously in that it derives optimally selected time-varying convergence factors such that the block mean square error is minimized from one iteration to the next. In particular, the algorithm developed here, called block modified covariance algorithm with individual adaptation of parameters (BMCAI), uses individual time-varying convergence factors computed using modified covariance matrix approximations along with the Gauss--Seidel method. Even though the BMCAI is gradient based, it retains the attractive spectral matching properties of fixed-window least-squares modified covariance algorithms while at the same time providing capabilities for time-varying spectral estimation.
Mathematics Subject Classification ID: 62M15
Mathematics Subject Classification ID: 65C60
Mathematics Subject Classification ID: 94A12
zbMATH DE Number: 1767947
MaRDI profile type: Publication
full work available at URL: s0165-1684(01)00178-5
OpenAlex ID: W2028826930
description: scientific article; zbMATH DE number 1767947