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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.

- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.8 pp.1536-1543

- Publication Date
- 1999/08/25

- Publicized

- Online ISSN

- DOI

- Type of Manuscript
- Special Section PAPER (Special Section on Digital Signal Processing)

- Category

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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 -