NDLI: A novel tracking analysis of the Normalized Least Mean Fourth algorithm

Content Provider	IEEE Xplore Digital Library
Author	Moinuddin, M. Zerguine, A.
Copyright Year	2011
Description	Author affiliation: Telecommunication Engineering Department, Iqra University, Pakistan (Moinuddin, M.) \|\| Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Saudi Arabia (Zerguine, A.)
Abstract	In this work, the tracking analysis of the Normalized Least Mean Fourth (NLMF) algorithm is investigated for a random walk channel under very weak assumptions. The novelty of this work resides in the fact that no restrictions are made on the dependence between the input successive regressors, the dependence among input regressor elements, the length of the adaptive filter, the distribution of noise and filter's input. Moreover, in our approach, there is no restriction made on the step size value and therefore the analysis holds for all the values of the step size in the range of stable NLMF algorithm. The analysis is based on a recently proposed performance measure called effective weight deviation vector which is the component of weight deviation vector in the direction of input regressor. In this paper, asymptotic time-averaged convergence for the mean square effective weight deviation, mean absolute excess estimation error, and the mean square excess estimation error for the NLMF algorithm are established. Finally, a number of simulation results are carried out to corroborate the theoretical findings.
Starting Page	4300
Ending Page	4303
File Size	164264
Page Count	4
File Format	PDF
ISBN	9781457705380
ISSN	15206149
e-ISBN	9781457705397
e-ISBN	9781457705373
DOI	10.1109/ICASSP.2011.5947304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-05-22
Publisher Place	Czech Republic
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Estimation error Noise Upper bound Simulation Convergence Adaptation models Convergence Analysis Adaptive filters NLMF algorithm
Content Type	Text
Resource Type	Article

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