Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal

Kai WANG; Man ZHOU; Lin ZHOU; Jiaying TU

doi:10.1587/transfun.E102.A.616

Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal

Kai WANG, Man ZHOU, Lin ZHOU, Jiaying TU

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Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E102-A No.4 pp.616-623

Publication Date: 2019/04/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E102.A.616

Type of Manuscript: PAPER

Category: Digital Signal Processing

Authors

Kai WANG
  Southeast University
Man ZHOU
  Southeast University
Lin ZHOU
  Southeast University
Jiaying TU
  Southeast University

Keyword

frequency estimation, autocorrelation function, least squares, reformulation of Pisarenko's method

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Kai WANG, Man ZHOU, Lin ZHOU, Jiaying TU, "Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal" in IEICE TRANSACTIONS on Fundamentals, vol. E102-A, no. 4, pp. 616-623, April 2019, doi: 10.1587/transfun.E102.A.616.
Abstract: Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E102.A.616/_p

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@ARTICLE{e102-a_4_616,
author={Kai WANG, Man ZHOU, Lin ZHOU, Jiaying TU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal},
year={2019},
volume={E102-A},
number={4},
pages={616-623},
abstract={Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.},
keywords={},
doi={10.1587/transfun.E102.A.616},
ISSN={1745-1337},
month={April},}

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TY - JOUR
TI - Two Novel Autocorrelation Based Methods for Frequency Estimation of Real Sinusoid Signal
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 616
EP - 623
AU - Kai WANG
AU - Man ZHOU
AU - Lin ZHOU
AU - Jiaying TU
PY - 2019
DO - 10.1587/transfun.E102.A.616
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E102-A
IS - 4
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - April 2019
AB - Many autocorrelation-based frequency estimation algorithms have been proposed. However, some of them cannot construct a strict linear prediction (LP) property among the adjacent autocorrelation lags, which affects the estimators' performance. To improve the precision of frequency estimation, two novel autocorrelation based frequency estimation methods of the real sinusoid signal in additive white Gaussian noise (AWGN) are proposed in this paper. Firstly, a simple method is introduced to transform the real sinusoid signal into the noncircular signal. Secondly, the autocorrelation of the noncircular signal is analyzed and a strict LP property is constructed among the adjacent autocorrelation lags of the noncircular signal. Thirdly, the least squares (LS) and reformed Pisarenko harmonic decomposer (RPHD) frameworks are employed to improve estimation accuracy. The simulation results match well with the theoretical values. In addition, computer simulations demonstrate that the proposed algorithm provides high estimation accuracy and good noise suppression capability.
ER -