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Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals
Abstract
The problem of both causal and noncausal identification of linear stochastic systems with quasiharmonically varying parameters is considered. The quasi-harmonic description allows one to model nonsinusoidal quasi-periodic parameter changes. The proposed identification algorithms are called generalized adaptive comb filters/smoothers because in the special signal case they reduce down to adaptive comb algorithms used to enhance or suppress nonstationary harmonic signals embedded in noise. The paper presents a thorough statistical analysis of generalized adaptive comb algorithms, and demonstrates their statistical efficiency in the case where the fundamental frequency of parameter changes varies slowly with time according to the integrated random-walk model.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.automatica.2013.02.037
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
AUTOMATICA
no. 49,
edition 6,
pages 1601 - 1613,
ISSN: 0005-1098 - Language:
- English
- Publication year:
- 2013
- Bibliographic description:
- Niedźwiecki M., Meller M.: Generalized adaptive comb filters/smoothers and their application to the identification of quasi-periodically varying systems and signals// AUTOMATICA. -Vol. 49, iss. 6 (2013), s.1601-1613
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.automatica.2013.02.037
- Verified by:
- Gdańsk University of Technology
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