Journal ArticleDOI
A variable step (VS) adaptive filter algorithm
R. Harris,D. Chabries,F. Bishop +2 more
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TLDR
It is shown that an upper bound for the convergence time is the classical mean-square-error time constant, and examples are given to demonstrate that for broad signal classes the convergenceTime is reduced by a factor of up to 50 in noise canceller applications for the proper selection of variable step parameters.Abstract:
In recent work, a new version of an LMS algorithm has been developed which implements a variable feedback constant μ for each weight of an adaptive transversal filter. This technique has been called the VS (variable step) algorithm and is an extension of earlier ideas in stochastic approximation for varying the step size in the method of steepest descents. The method may be implemented in hardware with only modest increases in complexity ( \approx 15 percent) over the LMS Widrow-Hoff algorithm. It is shown that an upper bound for the convergence time is the classical mean-square-error time constant, and examples are given to demonstrate that for broad signal classes (both narrow-band and broad-band) the convergence time is reduced by a factor of up to 50 in noise canceller applications for the proper selection of variable step parameters. Finally, the VS algorithm is applied to an IIR filter and simulations are presented for applications of the VS FIR and IIR adaptive filters.read more
Citations
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Journal ArticleDOI
Application of antenna arrays to mobile communications. II. Beam-forming and direction-of-arrival considerations
TL;DR: This paper provides a comprehensive and detailed treatment of different beam-forming schemes, adaptive algorithms to adjust the required weighting on antennas, direction-of-arrival estimation methods-including their performance comparison-and effects of errors on the performance of an array system, as well as schemes to alleviate them.
Book
Adaptive Filtering: Algorithms and Practical Implementation
TL;DR: Adaptive Filtering: Algorithms and Practical Implementation may be used as the principle text for courses on the subject, and serves as an excellent reference for professional engineers and researchers in the field.
Journal ArticleDOI
Active noise control: a tutorial review
Sen M. Kuo,Dennis R. Morgan +1 more
TL;DR: The basic adaptive algorithm for ANC is developed and analyzed based on single-channel broad-band feedforward control, then modified for narrow-bandFeedforward and adaptive feedback control, which are expanded to multiple-channel cases.
Journal ArticleDOI
A variable step size LMS algorithm
Raymond H. Kwong,E.W. Johnston +1 more
TL;DR: A least-mean-square adaptive filter with a variable step size, allowing the adaptive filter to track changes in the system as well as produce a small steady state error, is introduced.
Journal ArticleDOI
Mean-square performance of a convex combination of two adaptive filters
TL;DR: This paper studies the mean-square performance of a convex combination of two transversal filters and shows how the universality of the scheme can be exploited to design filters with improved tracking performance.
References
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Journal ArticleDOI
Adaptive noise cancelling: Principles and applications
Bernard Widrow,J.R. Glover,John M. McCool,J. Kaunitz,C.S. Williams,R.H. Hearn,James R. Zeidler,Jr. Eugene Dong,R.C. Goodlin +8 more
TL;DR: It is shown that in treating periodic interference the adaptive noise canceller acts as a notch filter with narrow bandwidth, infinite null, and the capability of tracking the exact frequency of the interference; in this case the canceller behaves as a linear, time-invariant system, with the adaptive filter converging on a dynamic rather than a static solution.
Journal ArticleDOI
Variational methods for the solution of problems of equilibrium and vibrations
TL;DR: The equivalence between boundary value problems of partial differential equations on the one hand and problems of the calculus of variations on the other hand has been a central point in analysis as mentioned in this paper.
Book ChapterDOI
Stationary and nonstationary learning characteristics of the LMS adaptive filter
TL;DR: It is shown that for stationary inputs the LMS adaptive algorithm, based on the method of steepest descent, approaches the theoretical limit of efficiency in terms of misadjustment and speed of adaptation when the eigenvalues of the input correlation matrix are equal or close in value.
Journal ArticleDOI
Fast, recursive-least-squares transversal filters for adaptive filtering
John M. Cioffi,Thomas Kailath +1 more
TL;DR: Fast transversal filter (FTF) implementations of recursive-least-squares (RLS) adaptive-filtering algorithms are presented in this paper and substantial improvements in transient behavior in comparison to stochastic-gradient or LMS adaptive algorithms are efficiently achieved by the presented algorithms.
Journal ArticleDOI
Accelerated Stochastic Approximation
TL;DR: In this article, the Robbins-Monro procedure and the Kiefer-Wolfowitz procedure are considered, for which the magnitude of the $n$th step depends on the number of changes in sign in $(X_i - X_{i - 1})$ for n = 2, \cdots, n.