A novel kurtosis driven variable step-size adaptive algorithm

A new variable step-size LMS filter is introduced. The time-varying step-size sequence is adjusted, utilizing the kurtosis of the estimation error, therefore reducing performance degradation due to the existence of strong noise. The convergence properties of the algorithm are analyzed, and an adapti...

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Veröffentlicht in:IEEE transactions on signal processing Jg. 47; H. 3; S. 864 - 872
Hauptverfasser: Pazaitis, D.I., Constantinides, A.G.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.1999
Institute of Electrical and Electronics Engineers
Schlagworte:
Adaptive algorithm
Adaptive algorithms
Adaptive filters
Adaptive signal processing
Algorithms
Applied sciences
Convergence
Detection, estimation, filtering, equalization, prediction
Equations
Exact sciences and technology
Filtering algorithms
Flexibility
Information, signal and communications theory
Kurtosis
Least squares methods
Noise
Numerical stability
Optimization
Signal and communications theory
Signal processing
Signal processing algorithms
Signal, noise
Statistics
Telecommunications and information theory
Transversal filters
Signal processing
Estimation error
Adaptive filter
Least squares method
ISSN:1053-587X
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Zusammenfassung:A new variable step-size LMS filter is introduced. The time-varying step-size sequence is adjusted, utilizing the kurtosis of the estimation error, therefore reducing performance degradation due to the existence of strong noise. The convergence properties of the algorithm are analyzed, and an adaptive kurtosis estimator that takes into account noise statistics and optimally adapts itself is also presented. Simulation results confirm the algorithm's improved performance and flexibility.
Bibliographie:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1053-587X
DOI:10.1109/78.747793