无人机集群全域时间同步的时隙聚合与拓扑聚合模型研究

王振岭; 陶海红; 魏海涛; 王正勇

doi:10.11999/JEIT251274

无人机集群全域时间同步的时隙聚合与拓扑聚合模型研究

doi: 10.11999/JEIT251274 cstr: 32379.14.JEIT251274

王振岭^{1, 2, ,},
陶海红²,
魏海涛³,
王正勇²

1.
西安电子科技大学雷达信号处理全国重点实验室西安 710071
2.
中国电子科技集团公司第五十四研究所石家庄 050081
3.
中国电子科技集团公司总体院北京 100041

详细信息

作者简介:
王振岭：男，研究员，研究方向为卫星导航、时间同步与应用技术

陶海红：女，教授，研究方向为雷达信号处理，分布式雷达系统

魏海涛：男，研究员，研究方向为时空基准服务、高精度时间同步技术

王正勇：男，高工，研究方向为时间同步技术，时频传递与比对技术

通讯作者:
王振岭　wangzlcti@163.com

中图分类号: TN967.2; TN914.52
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- 被引次数: 0
出版历程
- 收稿日期: 2025-12-01
- 修回日期: 2026-01-26
- 录用日期: 2026-01-27
- 网络出版日期: 2026-02-12

Research on Time Slots Aggregation and Topology Aggregation Model for Unmanned Aerial Vehicle Swarm Overall Time Synchronization

1.
National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
2.
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China
3.
General Institute of China Electronics Technology Group Corporation, Beijing 100041, China

摘要

摘要: 无人机(UAV)集群能够实现单平台无法完成的复杂任务，各节点之间的精密时间同步是无人机集群完成资源调度、协同定位以及数据融合的重要基础。随着UAV集群规模越来越大，UAV集群编队飞行中节点之间的时间比对链路连通性具有明显的时变特性，对连续、可靠的高精度时间同步实现提出了挑战。面向UAV集群全部节点的领导跟随一致性时间同步(LFCTS)，该文提出观测时隙聚合(OTSA)模型和时变拓扑聚合(TVTA)模型，并进行了误差建模与仿真分析。OTSA模型通过系统时间同步周期内多个时隙同步样本的有效利用，可有效提升全局时间同步的鲁棒性和时间同步精度，实现的同步精度优于2.56 ns，性能优于传统的分时比对同步体制。TVTA模型通过跨周期时间同步链路状态聚合和中继节点多跳时间同步措施，能够实现集群起飞集合、队形变换过程中的连续时间同步，典型的大规模集群全局同步的预测精度可优于8.60 ns，并基于小规模UAV集群飞行试验验正了模型的鲁棒性。所提方法能够为无人机集群的复杂协同应用提供必要保障。
- 无人机集群 /
- 时间同步 /
- 时变拓扑聚合 /
- 观测时隙聚合 /
- 双向时间比对
Abstract: Objective Unmanned Aerial Vehicle (UAV) swarms overcome the technical and performance limitations of individual UAVs and enable complex missions that cannot be accomplished by a single platform. High-precision time synchronization among swarm nodes serves as a fundamental requirement for key swarm operations, including resource scheduling, cooperative positioning, and multi-node data fusion. Existing research on UAV time synchronization mainly focuses on improving the accuracy of basic synchronization approaches. However, limitations remain in adapting to topological changes during swarm formation flights and in achieving global synchronization among multiple nodes. As the scale of UAV swarms increases, the connectivity of time-comparison links between nodes during formation flights exhibits clear time-varying characteristics. These characteristics create challenges for maintaining continuous, reliable, and precise overall time synchronization. To address stable formation flight and formation transformation scenarios in different mission stages of UAV swarms, an Observation Time Slots Aggregation (OTSA) model and a Time-Varying Topology Aggregation (TVTA) model are proposed to enhance the robustness of global time synchronization among swarm nodes and to improve Time Synchronization Accuracy (TSA). This study proposes an effective solution for Leader-Following Consistency Time Synchronization (LFCTS) in UAV swarms and provides references for time synchronization applications in heterogeneous and distributed systems. Methods Compared with the traditional Quasi Real-time Bidirectional Time Comparison (QRBTC) scheme, the time synchronization method based on the OTSA model fully uses all synchronization signal transmission and reception link resources within each time slot of the system synchronization period. Based on the “one transmission and multiple receptions” mechanism of all nodes, the Follower Node (FN) performs direct synchronization or single-hop indirect synchronization with the Leader Node (LN) in each time slot according to the OTSA model. This process produces tens of times more clock-skew observation samples than the traditional QRBTC scheme. The OTSA method improves the robustness of global time synchronization. It also enables secondary data processing using multi-slot synchronization samples, which further improves TSA compared with the QRBTC method. Based on the LFCTS results obtained during the system signal synchronization period, the TVTA model extends the direct comparison and single-hop indirect comparison mechanism of the OTSA model to cross-period multi-hop comparison. This extension addresses overall time synchronization instability caused by the time-varying characteristics of synchronization link relationships during UAV swarm takeoff, assembly, and formation transformation. Results and Discussions In the OTSA method, all time-comparison link resources of the total time slots are fully used during the synchronization period (Fig. 2). Based on the constructed error model and simulation analysis, for a UAV swarm with 50 nodes and a time slot allocation of 20 ms, time synchronization using the OTSA model achieves a single-slot TSA of 4.10～4.27 ns (Fig. 6). Within a complete time synchronization period, the overall TSA reaches 2.46～2.56 ns, which is better than the QRBTC scheme under the same conditions (Fig. 5(a)). The TVTA method uses cross-period synchronization comparison relationships to construct multi-hop time comparison links (Fig. 3 and 4). When the FN obtains external comparison relationships of other nodes through aggregation processing, one-way or two-way Dijkstra’s algorithm is applied to determine the multi-hop comparison link with optimal connectivity. Time tracing and comparison with the LN are then completed through edge computing. Error analysis indicates that during UAV swarm takeoff, assembly, and transitions to triangle or rhombus formations, time synchronization based on the TVTA model achieves an overall TSA better than 8.6 ns, which provides stronger global time synchronization capability. Conclusions This study addresses the robustness of time synchronization in UAV swarm formation flights. For stable formation flight and formation transformation scenarios during different mission stages, the OTSA and TVTA models are proposed. An error model is constructed and performance is analyzed. The results show the following. (1) The OTSA model improves the robustness of overall time synchronization through direct comparison and single-hop indirect comparison across multiple time slots within one synchronization period. The model achieves an overall TSA better than 2.56 ns and performs better than the traditional QRBTC method. (2) The TVTA model achieves overall UAV swarm time synchronization through multi-hop relay between nodes. Even when time-comparison links change, the model maintains global TSA better than 8.6 ns. (3) These two methods consider the time-varying characteristics of comparison links among UAV swarm nodes and have been verified through small-scale UAV swarm flight tests. They maintain synchronization robustness and performance and provide necessary support for coordinated UAV swarm operations. Future work will focus on practical flight verification, adaptation in complex scenarios, and further improvement of overall synchronization accuracy.
- Unmanned Aerial Vehicle (UAV) swarm /
- Time synchronization /
- Time-Varying Topology Aggregation (TVTA) /
- Observation Time Slots Aggregation (OTSA) /
- Bidirectional time comparison

HTML全文

图 1 QRBTC模式的无人机集群时间同步过程

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图 2 “1发多收”的时间同步观测过程

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图 3 不同比对周期的时变拓扑示意图

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图 4 时变拓扑聚合示意图

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图 5 传统时分比对方案下的全局TSA分布

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图 6 基于时隙聚合模型的典型时隙TSA

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图 7 基于TVTA模型的典型多跳LFCTS精度

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图 8 TVTA模型飞行试验UAV1钟差观测结果

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