An LSTM-Assisted Calibration Approach for Self-Time Transfer Performance Optimization in Multi-UAV Networks

Multi-Unmanned aerial vehicle (UAV) networks have already been deployed for complex tasks in both public and military domains. Central to the effectiveness of multi-UAV networks is self-time-synchronization (STS), which ensures that all UAVs operate based on a unified time reference where external time references are unavailable or signal strength is weak. The foundation of STS depends on the accurate acquisition of time-of-arrival (TOA) measurements. However, in wide-area sparse deployment scenarios, the increasing distance between UAVs reduces the signal-to-noise ratio (SNR), posing significant challenges to improving TOA estimation accuracy in multi-UAV networks. In this paper, we propose a calibration-assisted TOA estimation scheme that enables accurate self-time transfer. By incorporating modulated signals directly into the loop-based estimation process, the proposed scheme eliminates the reliance on pre-defined pilot structures, thereby enhancing adaptability to diverse modulation waveforms and significantly improving TOA estimation accuracy under dynamic signal conditions. To improve TOA estimation accuracy under low SNR conditions, we introduce a calibration scheme based on long short-term memory (LSTM) networks to refine current estimations. Numerical results demonstrate that the proposed algorithm outperforms existing approaches in terms of both accuracy and reliability, particularly under low SNR conditions. Compared to the existing method, our scheme achieves a 17.3<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement in the cumulative distribution function (CDF) at the time error of 3ns when SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$=$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-10$</tex-math></inline-formula> dB, indicating higher estimation accuracy.

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Publication Details

Journal
IEEE Transactions on Aerospace and Electronic Systems
Published
2025-08-29
DOI
https://doi.org/10.1109/taes.2025.3603798
Primary Topic
Network Time Synchronization Technologies
Type
article
Field-Weighted Citation Impact
0.00
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article

An LSTM-Assisted Calibration Approach for Self-Time Transfer Performance Optimization in Multi-UAV Networks

Changhao Du, Jianping An, Shuai Wang, Gaofeng Pan et al.
IEEE Transactions on Aerospace and Electronic Systems
Network Time Synchronization Technologies
article

An LSTM-Assisted Calibration Approach for Self-Time Transfer Performance Optimization in Multi-UAV Networks

Changhao Du, Jianping An, Shuai Wang, Gaofeng Pan, Dusit Niyato, Jiacheng Wang, Xin Liu
article en

Abstract

Multi-Unmanned aerial vehicle (UAV) networks have already been deployed for complex tasks in both public and military domains. Central to the effectiveness of multi-UAV networks is self-time-synchronization (STS), which ensures that all UAVs operate based on a unified time reference where external time references are unavailable or signal strength is weak. The foundation of STS depends on the accurate acquisition of time-of-arrival (TOA) measurements. However, in wide-area sparse deployment scenarios, the increasing distance between UAVs reduces the signal-to-noise ratio (SNR), posing significant challenges to improving TOA estimation accuracy in multi-UAV networks. In this paper, we propose a calibration-assisted TOA estimation scheme that enables accurate self-time transfer. By incorporating modulated signals directly into the loop-based estimation process, the proposed scheme eliminates the reliance on pre-defined pilot structures, thereby enhancing adaptability to diverse modulation waveforms and significantly improving TOA estimation accuracy under dynamic signal conditions. To improve TOA estimation accuracy under low SNR conditions, we introduce a calibration scheme based on long short-term memory (LSTM) networks to refine current estimations. Numerical results demonstrate that the proposed algorithm outperforms existing approaches in terms of both accuracy and reliability, particularly under low SNR conditions. Compared to the existing method, our scheme achieves a 17.3<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement in the cumulative distribution function (CDF) at the time error of 3ns when SNR <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$=$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-10$</tex-math></inline-formula> dB, indicating higher estimation accuracy.

IEEE Transactions on Aerospace and Electronic SystemsVol. 61(6)
Beijing Institute of Technology (CN), Nanyang Technological University (SG)
Affordable and clean energy
Openalex Percentile: Top 74%
Network Time Synchronization Technologies
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