Convergence analysis of adaptive filtering algorithms with singular data covariance matrix

The paper provides a rigorous analysis of the behavior of adaptive filtering algorithms when the covariance matrix of the filter input is singular. The analysis is done in the context of adaptive plant identification. The considered algorithms are LMS, RLS, sign (SA), and signed regressor (SRA) algo...

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Veröffentlicht in:	IEEE transactions on signal processing Jg. 49; H. 2; S. 334 - 343
1. Verfasser:	Eweda, E.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.02.2001 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive filters Algorithm design and analysis Algorithms Applied sciences Convergence Covariance matrix Detection, estimation, filtering, equalization, prediction Errors Exact sciences and technology Filtering algorithms Information, signal and communications theory Least squares approximation Mathematical analysis Regression analysis Resonance light scattering Signal and communications theory Signal processing algorithms Signal, noise Steady-state Studies Telecommunications and information theory Upper bound Vectors (mathematics) Theoretical study Adaptive filtering Adaptive signal processing Covariance matrix Algorithm Convergence Least mean squares methods
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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