基于信号到达时间建模的广域多点定位时间同步方法

汤新民; 周杨; 鲁其兴; 管祥民

doi:10.11999/JEIT240670

基于信号到达时间建模的广域多点定位时间同步方法

doi: 10.11999/JEIT240670

1.
中国民航大学城市空中交通系统技术与装备重点实验室天津 300300
2.
南京航空航天大学民航学院南京 211000
3.
中国民航管理干部学院民航通用航空运行重点实验室北京 100102

基金项目: 国家自然科学基金(52072174)，高端外国专家引进计划(G2023202003L)，天津市科技计划(24JCZDJC00090)

详细信息

作者简介:
汤新民：男，教授，博士，研究方向为新一代空中交通管制自动化系统、先进场面活动引导与控制系统、无人机运行服务与交通管理系统等

周杨：男，硕士生，研究方向为空中交通智能化技术、多点定位系统

鲁其兴：男，博士生，研究方向为先进场面活动引导与控制系统

管祥民：男，博士，副教授，研究领域为通用航空及无人机

通讯作者:
汤新民　tangxinmin@nuaa.edu.cn

中图分类号: TN95; U8
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- 被引次数: 0
出版历程
- 收稿日期: 2024-07-29
- 修回日期: 2025-05-09
- 网络出版日期: 2025-05-14
- 刊出日期: 2025-05-01

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

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)

摘要

摘要: 针对低空监视技术广域多点定位(WAM)时间同步困难或复杂度高，影响定位精度的问题，该文构建了基于到达时间(TOA)的时间同步及“同一消息”提取的数学模型，通过计算地面传感器的“同步启动时间”完成同步，计算复杂度低且易于实现。在此基础上，利用同一消息提取模型筛选出TOA用于定位计算。为了提高TOA估计值的精度，减小同步误差。提出了可变滑动滤波与卡尔曼滤波结合 (VMAF-Kalman)的联合滤波方法，提高可编程门阵列 (FPGA)基准时钟的稳定性，减小时钟延迟引起的TOA计数误差。仿真结果表明，联合滤波比单一滤波算法效果更好，TOA计数误差分别降低36.84%和25.36%。对无人机和民航飞机的定位测验结果都表现出较高的定位准确率，定位误差和位置更新速率符合标准要求，证明该文所提模型，具有实用性且有较好的同步精度。
- 广域多点定位系统 /
- 到达时间 /
- 联合滤波 /
- 时间同步 /
- 同一消息提取
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

HTML全文

图 1 本文理论流程图

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图 2 驯服时钟系统框架设计

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图 3 时间间隔计数原理

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图 4 同步启动时间结构图

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图 5 同步启动时间计算流程图

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图 6 消息组合提取示意图

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图 7 消息接收时间细化结构图

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图 8 同一消息时间关系结构图

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图 9 TDC计数原始相位差数据

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图 10 不同窗口大小算法滤波效果对比

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图 11 单一滤波和联合滤波的相位差滤波效果对比

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图 12 PID控制输出相位差结果

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图 13 TOA计数差值

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图 14 硬件端结构图

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图 15 无人机定位测试RMSE及偏差结果

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图 16 远端站实物及安放位置

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图 17 实际飞行轨迹与算法定位轨迹对比

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图 18 误差雷达图

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表 1 MAF和VMAF滤波结果

	N=8	N=16	N=32
MAF-SD/s (s)	4.977 6×10^–11	4.763 2×10^–11	4.658 2×10^–11
VMAF-SD/s (s)	4.794 5×10^–11	4.706 2×10^–11	4.646 1×10^–11
MAF-OF	9.726 5×10^–4	1.017 7×10^–3	1.040 7×10^–3
VMAF-OF	9.704 4×10^–4	9.836 2×10^–4	1.031 5×10^–3

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表 2 不同滤波算法的TOA计数差值

滤波方法	TOA计数平均差值 (时钟周期)(个)	TOA计数平均差值 (时间)(ns)
联合滤波	1.56	3.902
Kalman滤波	2.47	6.175
VMAF	2.09	5.225

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表 3 不同定位算法定位结果

	Chan算法	Chan-Taylor组合算法	Fang算法
$ {\bar s_{\mathrm{h}}} $(m)	136.630 8	101.571 8	120.481 2
$ {\bar s_{\mathrm{v}}} $(m)	174.502 6	131.751 7	160.341 8
$ \eta $(%)	80.44	87.02	82.58
$ t $(s)	3.42	4.18	3.73

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