Quantifying the effects of dimension on the convergence rate of the LMS adaptive FIR estimator

The convergence rate of an LMS adaptive FIR filter to an unknown stationary channel may be influenced by the filter parameter dimension as well as by the input signal's characteristics. This dimension influence may be of importance in applications, such as adaptive acoustic echo cancellation, i...

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Bibliographic Details
Published in:IEEE transactions on signal processing 1998-10, Vol.46 (10), p.2611-2615
Main Authors: Homer, J., Bitmead, R.R., Mareels, I.
Format: Article
Language:English
Subjects:
Acoustic applications
Acoustic measurements
Adaptive filters
Applied sciences
Autocorrelation
Convergence
Detection, estimation, filtering, equalization, prediction
Echo cancellers
Exact sciences and technology
Finite impulse response filter
Information, signal and communications theory
Least squares approximation
Proposals
Signal analysis
Signal and communications theory
Signal, noise
Telecommunications and information theory
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Summary:The convergence rate of an LMS adaptive FIR filter to an unknown stationary channel may be influenced by the filter parameter dimension as well as by the input signal's characteristics. This dimension influence may be of importance in applications, such as adaptive acoustic echo cancellation, in which the unknown channel is typically modeled as a "long" FIR filter. The paper includes the development and proposal of a novel measure of the expected convergence rate of the LMS/FIR filter followed by analysis of this convergence rate measure. The analysis indicates that unless the input signal is white, the expected convergence rate decreases with increasing dimension down to a limiting value, which is determined by the input signal's autocorrelation level.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.720364