The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Cet article a étudié un algorithme d'estimation du délai de sous-échantillon (STE) basé sur l'amplitude de la fonction de corrélation croisée pour améliorer la précision de l'estimation. Dans cet article, une estimation approximative du retard est appliquée sur la base d'un corrélateur croisé traditionnel, et une estimation fine est obtenue en rapprochant la séquence de corrélation croisée échantillonnée de l'amplitude de la fonction de corrélation croisée théorique pour le signal de modulation de fréquence linéaire (LFM). Les résultats de simulation montrent que l'algorithme proposé surpasse les méthodes existantes et peut améliorer efficacement la précision de l'estimation du délai avec une complexité comparable à la méthode traditionnelle de corrélation croisée. La limite théorique de Cramér-Rao (CRB) est dérivée et les simulations démontrent que les performances du STE peuvent s'approcher de la limite. Finalement, quatre paramètres importants ont été discutés dans la simulation pour explorer l'impact sur l'erreur quadratique moyenne (MSE).
Cui YANG
South China University of Technology
Yalu XU
South China University of Technology
Yue YU
the E Surfing Internet of Things Technology Company Limited
Gengxin NING
South China University of Technology
Xiaowu ZHU
Lands and Resource Department of Guangdong Province
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Cui YANG, Yalu XU, Yue YU, Gengxin NING, Xiaowu ZHU, "A New Subsample Time Delay Estimation Algorithm for LFM-Based Detection" in IEICE TRANSACTIONS on Fundamentals,
vol. E106-A, no. 3, pp. 575-581, March 2023, doi: 10.1587/transfun.2022EAP1025.
Abstract: This paper investigated a Subsample Time delay Estimation (STE) algorithm based on the amplitude of cross-correlation function to improve the estimation accuracy. In this paper, a rough time delay estimation is applied based on traditional cross correlator, and a fine estimation is achieved by approximating the sampled cross-correlation sequence to the amplitude of the theoretical cross-correlation function for linear frequency modulation (LFM) signal. Simulation results show that the proposed algorithm outperforms existing methods and can effectively improve time delay estimation accuracy with the complexity comparable to the traditional cross-correlation method. The theoretical Cramér-Rao Bound (CRB) is derived, and simulations demonstrate that the performance of STE can approach the boundary. Eventually, four important parameters discussed in the simulation to explore the impact on Mean Squared Error (MSE).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2022EAP1025/_p
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@ARTICLE{e106-a_3_575,
author={Cui YANG, Yalu XU, Yue YU, Gengxin NING, Xiaowu ZHU, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A New Subsample Time Delay Estimation Algorithm for LFM-Based Detection},
year={2023},
volume={E106-A},
number={3},
pages={575-581},
abstract={This paper investigated a Subsample Time delay Estimation (STE) algorithm based on the amplitude of cross-correlation function to improve the estimation accuracy. In this paper, a rough time delay estimation is applied based on traditional cross correlator, and a fine estimation is achieved by approximating the sampled cross-correlation sequence to the amplitude of the theoretical cross-correlation function for linear frequency modulation (LFM) signal. Simulation results show that the proposed algorithm outperforms existing methods and can effectively improve time delay estimation accuracy with the complexity comparable to the traditional cross-correlation method. The theoretical Cramér-Rao Bound (CRB) is derived, and simulations demonstrate that the performance of STE can approach the boundary. Eventually, four important parameters discussed in the simulation to explore the impact on Mean Squared Error (MSE).},
keywords={},
doi={10.1587/transfun.2022EAP1025},
ISSN={1745-1337},
month={March},}
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TY - JOUR
TI - A New Subsample Time Delay Estimation Algorithm for LFM-Based Detection
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 575
EP - 581
AU - Cui YANG
AU - Yalu XU
AU - Yue YU
AU - Gengxin NING
AU - Xiaowu ZHU
PY - 2023
DO - 10.1587/transfun.2022EAP1025
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E106-A
IS - 3
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - March 2023
AB - This paper investigated a Subsample Time delay Estimation (STE) algorithm based on the amplitude of cross-correlation function to improve the estimation accuracy. In this paper, a rough time delay estimation is applied based on traditional cross correlator, and a fine estimation is achieved by approximating the sampled cross-correlation sequence to the amplitude of the theoretical cross-correlation function for linear frequency modulation (LFM) signal. Simulation results show that the proposed algorithm outperforms existing methods and can effectively improve time delay estimation accuracy with the complexity comparable to the traditional cross-correlation method. The theoretical Cramér-Rao Bound (CRB) is derived, and simulations demonstrate that the performance of STE can approach the boundary. Eventually, four important parameters discussed in the simulation to explore the impact on Mean Squared Error (MSE).
ER -