Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.
Kai WANG
Southeast University
Jiaying DING
Southeast University
Yili XIA
Southeast University
Xu LIU
Southeast University
Jinguang HAO
Ludong University
Wenjiang PEI
Southeast University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Copy
Kai WANG, Jiaying DING, Yili XIA, Xu LIU, Jinguang HAO, Wenjiang PEI, "Two High Accuracy Frequency Estimation Algorithms Based on New Autocorrelation-Like Function for Noncircular/Sinusoid Signal" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 7, pp. 1065-1073, July 2018, doi: 10.1587/transfun.E101.A.1065.
Abstract: Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1065/_p
Copy
@ARTICLE{e101-a_7_1065,
author={Kai WANG, Jiaying DING, Yili XIA, Xu LIU, Jinguang HAO, Wenjiang PEI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Two High Accuracy Frequency Estimation Algorithms Based on New Autocorrelation-Like Function for Noncircular/Sinusoid Signal},
year={2018},
volume={E101-A},
number={7},
pages={1065-1073},
abstract={Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.},
keywords={},
doi={10.1587/transfun.E101.A.1065},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Two High Accuracy Frequency Estimation Algorithms Based on New Autocorrelation-Like Function for Noncircular/Sinusoid Signal
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1065
EP - 1073
AU - Kai WANG
AU - Jiaying DING
AU - Yili XIA
AU - Xu LIU
AU - Jinguang HAO
AU - Wenjiang PEI
PY - 2018
DO - 10.1587/transfun.E101.A.1065
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2018
AB - Computing autocorrelation coefficient can effectively reduce the influence of additive white noise, thus estimation precision will be improved. In this paper, an autocorrelation-like function, different from the ordinary one, is defined, and is proven to own better linear predictive performance. Two algorithms for signal model are developed to achieve frequency estimates. We analyze the theoretical properties of the algorithms in the additive white Gaussian noise. The simulation results match with the theoretical values well in the sense of mean square error. The proposed algorithms compare with existing estimators, are closer to the Cramer-Rao bound (CRLB). In addition, computer simulations demonstrate that the proposed algorithms provide high accuracy and good anti-noise capability.
ER -