IEICE TRANSACTIONS on Fundamentals
Non-iterative Frequency Estimator Based on Approximation of the Wiener-Khinchin Theorem
Summary :
A closed form frequency estimator is derived for estimating the frequency of a complex exponential signal, embedded in white Gaussian noise. The new estimator consists of the fast Fourier transform (FFT) as the coarse estimation and the phase of autocorrelation lags as the fine-frequency estimator. In the fine-frequency estimation, autocorrelations are calculated from the power-spectral density of the signal, based on the Wiener-Khinchin theorem. For simplicity and suppressing the effect of noise, only the spectrum lines around the actual tone are used. Simulation results show that, the performance of the proposed estimator is approaching the Cramer-Rao Bound (CRB), and has a lower SNR threshold compared with other existing estimators.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E98-A No.4 pp.1021-1025
- Publication Date
- 2015/04/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E98.A.1021
- Type of Manuscript
- LETTER
- Category
- Digital Signal Processing
Authors
Cui YANG
South China University of Technology
Lingjun LIU
South China University of Technology
Keyword
Latest Issue
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Cui YANG, Lingjun LIU, "Non-iterative Frequency Estimator Based on Approximation of the Wiener-Khinchin Theorem" in IEICE TRANSACTIONS on Fundamentals,
vol. E98-A, no. 4, pp. 1021-1025, April 2015, doi: 10.1587/transfun.E98.A.1021.
Abstract: A closed form frequency estimator is derived for estimating the frequency of a complex exponential signal, embedded in white Gaussian noise. The new estimator consists of the fast Fourier transform (FFT) as the coarse estimation and the phase of autocorrelation lags as the fine-frequency estimator. In the fine-frequency estimation, autocorrelations are calculated from the power-spectral density of the signal, based on the Wiener-Khinchin theorem. For simplicity and suppressing the effect of noise, only the spectrum lines around the actual tone are used. Simulation results show that, the performance of the proposed estimator is approaching the Cramer-Rao Bound (CRB), and has a lower SNR threshold compared with other existing estimators.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E98.A.1021/_p
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@ARTICLE{e98-a_4_1021,
author={Cui YANG, Lingjun LIU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Non-iterative Frequency Estimator Based on Approximation of the Wiener-Khinchin Theorem},
year={2015},
volume={E98-A},
number={4},
pages={1021-1025},
abstract={A closed form frequency estimator is derived for estimating the frequency of a complex exponential signal, embedded in white Gaussian noise. The new estimator consists of the fast Fourier transform (FFT) as the coarse estimation and the phase of autocorrelation lags as the fine-frequency estimator. In the fine-frequency estimation, autocorrelations are calculated from the power-spectral density of the signal, based on the Wiener-Khinchin theorem. For simplicity and suppressing the effect of noise, only the spectrum lines around the actual tone are used. Simulation results show that, the performance of the proposed estimator is approaching the Cramer-Rao Bound (CRB), and has a lower SNR threshold compared with other existing estimators.},
keywords={},
doi={10.1587/transfun.E98.A.1021},
ISSN={1745-1337},
month={April},}
Copy
TY - JOUR
TI - Non-iterative Frequency Estimator Based on Approximation of the Wiener-Khinchin Theorem
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1021
EP - 1025
AU - Cui YANG
AU - Lingjun LIU
PY - 2015
DO - 10.1587/transfun.E98.A.1021
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E98-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2015
AB - A closed form frequency estimator is derived for estimating the frequency of a complex exponential signal, embedded in white Gaussian noise. The new estimator consists of the fast Fourier transform (FFT) as the coarse estimation and the phase of autocorrelation lags as the fine-frequency estimator. In the fine-frequency estimation, autocorrelations are calculated from the power-spectral density of the signal, based on the Wiener-Khinchin theorem. For simplicity and suppressing the effect of noise, only the spectrum lines around the actual tone are used. Simulation results show that, the performance of the proposed estimator is approaching the Cramer-Rao Bound (CRB), and has a lower SNR threshold compared with other existing estimators.
ER -
English
