Robust adaptive filtering algorithms for α-stable random processes
Author
Aydin, G.
Arıkan, Orhan
Çetin, A. Enis
Date
1999-02Source Title
IEEE Transactions on Circuits and Systems II : Analog and Digital Signal Processing
Print ISSN
1057-7130
Publisher
Institute of Electrical and Electronics Engineers
Volume
46
Issue
2
Pages
198 - 202
Language
English
Type
ArticleItem Usage Stats
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Abstract
A new class of algorithms based on the fractional lower order statistics is proposed for finite-impulse response adaptive filtering in the presence of α-stable processes. It is shown that the normalized least mean p-norm (NLMP) and Douglas' family of normalized least mean square algorithms are special cases of the proposed class of algorithms. A convergence proof for the new algorithm is given by showing that it performs a descent-type update of the NLMP cost function. Simulation studies indicate that the proposed algorithms provide superior performance in impulsive noise environments compared to the existing approaches.
Keywords
Adaptive filteringFIR filters
Mathematical models
Probability density function
Random processes
Spurious signal noise
Stability
Statistical methods
Alpha stable random processes
Finite impulse response adaptive filtering
Impulsive signals
Least mean square algorithms
Normalized least mean p norm
Adaptive algorithms