Adaptive modified covariance algorithms for spectral analysis

Kyriakos Kitsios, Andreas Spanias, Bruno Welfert

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)715-720
Number of pages6
JournalSignal Processing
Volume82
Issue number5
DOIs
StatePublished - May 1 2002

Keywords

  • Modified covariance algorithms
  • Spectral analysis

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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