Adaptive modified covariance algorithm for spectral analysis

Kyriakos Kitsios, Andreas Spanias, Bruno Welfert, Philipos Loizou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 [3] in that it uses 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)
Title of host publicationIEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP
Editors Anon
Place of PublicationLos Alamitos, CA, United States
PublisherIEEE
Pages56-59
Number of pages4
StatePublished - 1996
EventProceedings of the 1996 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP'96 - Corfu, Greece
Duration: Jun 24 1996Jun 26 1996

Other

OtherProceedings of the 1996 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP'96
CityCorfu, Greece
Period6/24/966/26/96

ASJC Scopus subject areas

  • Engineering(all)

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    Kitsios, K., Spanias, A., Welfert, B., & Loizou, P. (1996). Adaptive modified covariance algorithm for spectral analysis. In Anon (Ed.), IEEE Signal Processing Workshop on Statistical Signal and Array Processing, SSAP (pp. 56-59). IEEE.