Wide-Area Multilateration Time Synchronization Method Based on Signal Arrival Time Modeling

TANG Xinmin; ZHOU Yang; LU Qixing; GUAN Xiangmin

doi:10.11999/JEIT240670

Volume 47 Issue 5

May 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(5): 1434-1449

TANG Xinmin, ZHOU Yang, LU Qixing, GUAN Xiangmin. Wide-Area Multilateration Time Synchronization Method Based on Signal Arrival Time Modeling[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1434-1449. doi: 10.11999/JEIT240670

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TANG Xinmin, ZHOU Yang, LU Qixing, GUAN Xiangmin. Wide-Area Multilateration Time Synchronization Method Based on Signal Arrival Time Modeling[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1434-1449. doi: 10.11999/JEIT240670

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Wide-Area Multilateration Time Synchronization Method Based on Signal Arrival Time Modeling

doi: 10.11999/JEIT240670 cstr: 32379.14.JEIT240670

1.
Key Laboratory of Urban Air Traffic System Technology and Equipment, Civil Aviation University of China Tianjin300300, China
2.
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3.
Key Laboratory of Civil Aviation General Aviation Operation, Civil Aviation Management Institute of China, Beijing 100102, China

Funds: The National Natural Science Foundation of China (52072174), High-end Foreign Experts Re-cruitment Program (G2023202003L), Tianjin Science and Technology Plan (24JCZDJC00090)

Received Date: 2024-07-29
Rev Recd Date: 2025-05-09

Available Online: 2025-05-14

Publish Date: 2025-05-01

Abstract

Abstract

Objective Wide-Area Multilateration (WAM), a high-precision positioning technology currently under nationwide deployment, is widely applied in aircraft positioning on airport surfaces and in terminal areas. However, as WAM depends on collaborative signal processing across multiple stations, challenges such as time synchronization and computational complexity continue to constrain positioning accuracy. This study develops a mathematical model for time synchronization and “same-message” extraction based on Time Of Arrival (TOA), achieving synchronization by calculating the “synchronized start time” of ground sensors. The proposed method offers low computational complexity and is straightforward to implement. To enhance TOA estimation accuracy and reduce synchronization error, a joint filtering strategy—Variable Moving Average Filtering and Kalman (VMAF-Kalman)—is proposed to minimize TOA counting deviations introduced by clock drift. The model addresses synchronization challenges in distributed station deployments and employs joint filtering to correct initial clock source deviations. Methods This study addresses the challenge of high-precision TOA acquisition by proposing a joint filtering method that combines VMAF-Kalman. This approach filters the phase difference count between the GPS 1 Pulse Per Second (1PPS) signal and the local crystal oscillator 1PPS signal, producing a stable reference clock to mitigate the effects of noise and oscillator aging that induce clock drift. Therefore, stable TOA counting with a precision of 2.5 ns is achieved in Field-Programmable Gate Arrays (FPGAs). To resolve synchronization issues in distributed WAM systems, a time synchronization model based on TOA is proposed, which determines the synchronized start time of remote stations. Additionally, a same-message extraction model is developed to identify the TOA of identical messages, enabling accurate multilateration positioning. Results and Discussions Two experiments evaluate the proposed method and model: a filtering performance comparison and an actual flight trajectory positioning experiment for time synchronization validation. The latter includes two simulation scenarios: Scenario 1 consists of drone positioning tests, and Scenario 2 consists of civil aviation aircraft positioning tests. The simulation results indicate that the joint filtering method outperforms single filtering approaches, reducing TOA counting errors by 36.84% and 25.36% in the respective scenarios. Both the drone and civil aviation tests demonstrate high positioning accuracy, with errors and update rates meeting standard requirements. These findings confirm the practicality of the proposed method and the improved synchronization accuracy of the model. Conclusions Firstly, the proposed VMAF-Kalman joint filtering method demonstrates clear advantages over single filtering algorithms in both performance and hardware efficiency. Simulation results show that the output of the PID controller remains within a narrower fluctuation range, while TOA counting errors are reduced by 36.84% and 25.36%, respectively. These findings confirm that joint filtering stabilizes clock signals, improves TOA counting accuracy in FPGAs, and reduces synchronization errors. Secondly, the time synchronization and same-message extraction models developed in this study simplify existing synchronization methods by enabling WAM synchronization and TOA extraction through algorithmic computation alone. Simulations incorporating actual flight data in low-altitude airspace, verified across multiple positioning algorithms, further validate the model. Drone test results show that vertical Root Mean Square Error (RMSE) and deviation remain within 20 m, with horizontal RMSE below 10 m. For civil aviation aircraft, all algorithms achieved accuracy rates above 80%, with average errors under 300 m and position update intervals within 5 s, meeting established standards. The experimental outcomes confirm the feasibility and applicability of the proposed model for high-precision WAM time synchronization.
- Wide-Area Multilateration (WAM) system,
- Time Of Arrival (TOA),
- Joint filtering,
- Time synchronization,
- Same-message extraction

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References(35)

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