IEICE TRANSACTIONS on Fundamentals
Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection
Summary :
This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.
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- IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.8 pp.1536-1543
- Publication Date
- 1999/08/25
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- Special Section PAPER (Special Section on Digital Signal Processing)
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Koji MATSUURA, Eiji WATANABE, Akinori NISHIHARA, "Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E82-A, no. 8, pp. 1536-1543, August 1999, doi: .
Abstract: This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e82-a_8_1536/_p
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@ARTICLE{e82-a_8_1536,
author={Koji MATSUURA, Eiji WATANABE, Akinori NISHIHARA, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection},
year={1999},
volume={E82-A},
number={8},
pages={1536-1543},
abstract={This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.},
keywords={},
doi={},
ISSN={},
month={August},}
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TY - JOUR
TI - Adaptive Line Enhancers on the Basis of Least-Squares Algorithm for a Single Sinusoid Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1536
EP - 1543
AU - Koji MATSUURA
AU - Eiji WATANABE
AU - Akinori NISHIHARA
PY - 1999
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E82-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 1999
AB - This paper proposes adaptive line enhancers with new coefficient update algorithms on the basis of least-square-error criteria. Adaptive algorithms by least-squares are known to converge faster than stochastic-gradient ones. However they have high computational complexity due to matrix inversion. To avoid matrix inversion the proposed algorithms adapt only one coefficient to detect one sinusoid. Both FIR and IIR types of adaptive algorithm are presented, and the techniques to reduce the influence of additive noise is described in this paper. The proposed adaptive line enhancers have simple structures and show excellent convergence characteristics. While the convergence of gradient-based algorithms largely depend on their stepsize parameters, the proposed ones are free from them.
ER -
English
