A variable step size LMS algorithm

A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and stea...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1992-07, Vol.40 (7), p.1633-1642
Main Authors: Kwong, R.H., Johnston, E.W.
Format: Article
Language:English
Subjects:
Adaptive filters
Algorithm design and analysis
Analytical models
Convergence
Equations
Least squares approximation
Performance analysis
Signal processing
Steady-state
System identification
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Summary:A least-mean-square (LMS) adaptive filter with a variable step size is introduced. The step size increases or decreases as the mean-square error increases or decreases, allowing the adaptive filter to track changes in the system as well as produce a small steady state error. The convergence and steady-state behavior of the algorithm are analyzed. The results reduce to well-known results when specialized to the constant-step-size case. Simulation results are presented to support the analysis and to compare the performance of the algorithm with the usual LMS algorithm and another variable-step-size algorithm. They show that its performance compares favorably with these existing algorithms.< >
ISSN:1053-587X
1941-0476
DOI:10.1109/78.143435