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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 추정 정확도를 향상시키기 위해 상호 상관 함수의 진폭을 기반으로 하는 STE(Subsample Time Delay Estimation) 알고리즘을 조사했습니다. 본 논문에서는 전통적인 교차 상관기를 기반으로 대략적인 시간 지연 추정을 적용하고 샘플링된 교차 상관 시퀀스를 선형 주파수 변조(LFM) 신호에 대한 이론적 교차 상관 함수의 진폭으로 근사함으로써 정밀한 추정을 달성합니다. 시뮬레이션 결과는 제안된 알고리즘이 기존 방법보다 성능이 뛰어나고 기존 상호상관 방법에 필적하는 복잡성으로 시간 지연 추정 정확도를 효과적으로 향상시킬 수 있음을 보여줍니다. 이론적인 CRB(Cramér-Rao Bound)가 도출되었으며, 시뮬레이션을 통해 STE의 성능이 경계에 접근할 수 있음을 보여줍니다. 결국, 평균 제곱 오차(MSE)에 미치는 영향을 조사하기 위해 시뮬레이션에서 네 가지 중요한 매개변수가 논의되었습니다.
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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부
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
부
@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},}
부
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 -