A Variable Step-Size Proportionate NLMS Algorithm for Identification of Sparse Impulse Response
Summary :
Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E93-A No.1 pp.233-242
- Publication Date
- 2010/01/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E93.A.233
- Type of Manuscript
- PAPER
- Category
- Digital Signal Processing
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Ligang LIU, Masahiro FUKUMOTO, Sachio SAIKI, Shiyong ZHANG, "A Variable Step-Size Proportionate NLMS Algorithm for Identification of Sparse Impulse Response" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 1, pp. 233-242, January 2010, doi: 10.1587/transfun.E93.A.233.
Abstract: Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.233/_p
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@ARTICLE{e93-a_1_233,
author={Ligang LIU, Masahiro FUKUMOTO, Sachio SAIKI, Shiyong ZHANG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Variable Step-Size Proportionate NLMS Algorithm for Identification of Sparse Impulse Response},
year={2010},
volume={E93-A},
number={1},
pages={233-242},
abstract={Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.},
keywords={},
doi={10.1587/transfun.E93.A.233},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - A Variable Step-Size Proportionate NLMS Algorithm for Identification of Sparse Impulse Response
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 233
EP - 242
AU - Ligang LIU
AU - Masahiro FUKUMOTO
AU - Sachio SAIKI
AU - Shiyong ZHANG
PY - 2010
DO - 10.1587/transfun.E93.A.233
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2010
AB - Recently, proportionate adaptive algorithms have been proposed to speed up convergence in the identification of sparse impulse response. Although they can improve convergence for sparse impulse responses, the steady-state misalignment is limited by the constant step-size parameter. In this article, based on the principle of least perturbation, we first present a derivation of normalized version of proportionate algorithms. Then by taking the disturbance signal into account, we propose a variable step-size proportionate NLMS algorithm to combine the benefits of both variable step-size algorithms and proportionate algorithms. The proposed approach can achieve fast convergence with a large step size when the identification error is large, and then considerably decrease the steady-state misalignment with a small step size after the adaptive filter reaches a certain degree of convergence. Simulation results verify the effectiveness of the proposed approach.
ER -
English
