IEICE TRANSACTIONS on Fundamentals
Performance Improvement of Variable Stepsize NLMS
Summary :
Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.
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- IEICE TRANSACTIONS on Fundamentals Vol.E78-A No.8 pp.905-914
- Publication Date
- 1995/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Jirasak TANPREEYACHAYA, Ichi TAKUMI, Masayasu HATA, "Performance Improvement of Variable Stepsize NLMS" in IEICE TRANSACTIONS on Fundamentals,
vol. E78-A, no. 8, pp. 905-914, August 1995, doi: .
Abstract: Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e78-a_8_905/_p
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@ARTICLE{e78-a_8_905,
author={Jirasak TANPREEYACHAYA, Ichi TAKUMI, Masayasu HATA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Performance Improvement of Variable Stepsize NLMS},
year={1995},
volume={E78-A},
number={8},
pages={905-914},
abstract={Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Performance Improvement of Variable Stepsize NLMS
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 905
EP - 914
AU - Jirasak TANPREEYACHAYA
AU - Ichi TAKUMI
AU - Masayasu HATA
PY - 1995
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E78-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1995
AB - Improvement of the convergence characteristics of the NLMS algorithm has received attention in the area of adaptive filtering. A new variable stepsize NLMS method, in which the stepsize is updated optimally by using variances of the measured error signal and the estimated noise, is proposed. The optimal control equation of the stepsize has been derived from a convergence characteristic approximation. A new condition to judge convergence is introduced in this paper to ensure the fastest initial convergence speed by providing precise timing to start estimating noise level. And further, some adaptive smoothing devices have been added into the ADF to overcome the saturation problem of the identification error caused by some random deviations. By the simulation, The initial convergence speed and the identification error in precise identification mode is improved significantly by more precise adjustment of stepsize without increasing in computational cost. The results are the best ever reported performanced. This variable stepsize NLMS-ADF also shows good effectiveness even in severe conditions, such as noisy or fast changing circumstances.
ER -
English
