数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (5): 72-83.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.05.006

doi: 10.11871/jfdc.issn.2096-742X.2026.05.006

• 专刊：“东方”超级计算系统，助力青年科学家科技创新 • 上一篇    下一篇

基于ADS-B机制的低空无人机监视容量研究

王春琦1,2(),刘玉佩3,胡旭东4,王洁1,钱肇钧3,杨璐瑞2,朱秋明1,*()   

  1. 1 南京航空航天大学， 电子信息工程学院， 江苏 南京 210016
    2 国家无线电监测中心成都监测站， 四川 成都 611100
    3 国家无线电监测中心， 北京 100037
    4 中国科学院空天信息创新研究院， 目标认知与应用技术国家级重点实验室， 北京 100094
  • 收稿日期:2026-05-11 出版日期:2026-10-20 发布日期:2026-10-20
  • 通讯作者: *朱秋明（E-mail: zhuqiuming@nuaa.edu.cn）
  • 作者简介:王春琦，南京航空航天大学，博士研究生，国家无线电监测中心成都监测站，工程师。主要研究方向为动态频谱共享、无线信道建模。
    本文承担的工作为模型设计，模型算法实现。
    WANG Chunqi is an engineer at Chengdu Monitoring Station of the State Radio Monitoring Center. He is currently pursuing the Ph.D. degree in spectrum monitoring and management with the College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics. His main research interests are dynamic spectrum sharing and wireless channel modeling.
    In this paper, he is mainly responsible for model design and model algorithm implementation. E-mail: wangchunqi@nuaa.edu.cn|朱秋明，南京航空航天大学，博士生导师，主要研究方向为低空网络的信道探测、建模与仿真，无人机信道大数据策略应用和航天测控电磁环境评估及建模。
    本文承担的工作为指导模型构建和优化仿真策略。
    ZHU Qiuming is a Ph. D. supervisor at Nanjing University of Aeronautics and Astronautics (NUAA). His main research interests are channel sounding, modeling, and simulation for low-altitude networks, applications of big data strategies for UAV channels, and assessment and modeling of the electromagnetic environment for aerospace telemetry and control.
    In this paper, he is mainly responsible for providing the guidance for design computational models and optimizing simulation strategies. E-mail: zhuqiuming@nuaa.edu.cn
  • 基金资助:
    国家自然科学基金(62271250);“东方之星”青年科学家创新基金(DFZX202514);自然资源部地理国情监测重点实验室开放基金资助课题(2025NGCM05)

Research on Low-Altitude UAV Surveillance Capacity Based on ADS-B Mechanism

WANG Chunqi1,2(),LIU Yupei3,HU Xudong4,WANG Jie1,QIAN Zhaojun3,YANG Lurui2,ZHU Qiuming1,*()   

  1. 1 College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China
    2 Chengdu Monitoring Station of the State Radio Monitoring Center, Chengdu, Sichuan 611100, China
    3 State Radio Monitoring Center, Beijing 100037, China
    4 The Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
  • Received:2026-05-11 Online:2026-10-20 Published:2026-10-20

RichHTML

1

PDF

5

摘要：

【目的】针对低空高密度无人机场景下传统 ADS-B 系统容量不足、场景适配性差、未考虑多径衰落与编队协同干扰等问题，旨在建立融合场景适配与跨层协同的监视容量计算模型，提升复杂低空环境下 ADS-B 监视容量评估准确性与实用性。【方法】引入编队业务相关性与多径衰落误码率等场景化参数，构建干扰消除系数与任务优先级双层跨层优化框架，提出面向低空无人机的报文接收率与监视容量关联模型，并根据民用无人驾驶航空器运行识别最低性能要求完成仿真验证。【结果】仿真表明，通过干扰消除、编队解耦、优先级调度及抗多径优化等措施，系统容量最大可提升200%以上。【结论】该模型可有效评估低空ADS-B 监视容量，明确关键参数影响与优化方向，为无人机运行识别播发系统设计与低空空域安全监管提供理论支撑。

关键词: 低空无人机, ADS-B监视容量, 报文接收率, 运行识别播发

Abstract:

[Objective] To address the issues of limited capacity, insufficient scenario adaptability, and neglection of multipath fading and formation cooperative interference in conventional ADS-B systems for low-altitude high-density UAV scenarios, this study develops a surveillance capacity calculation model integrating scenario adaptation and cross-layer coordination to improve the accuracy and applicability of ADS-B capacity assessment in complex low-altitude environments. [Methods] Scenario-based parameters such as formation service correlation and multipath fading bit error rate are introduced. A dual cross-layer optimization framework with interference cancellation coefficient and task priority is constructed, and a correlation model between message reception rate and surveillance capacity for low-altitude UAVs is established. Simulations are conducted in compliance with the minimum performance requirements for operation identification of civil unmanned aircraft systems. [Results] Simulation results demonstrate that system capacity can be boosted by over 200% through interference cancellation, formation decoupling, priority scheduling, and anti-multipath optimization. [Conclusions] The proposed model can effectively evaluate ADS-B surveillance capacity in low-altitude environments, identify key parameter impacts and optimization strategies, and provide theoretical support for the design of UAV operation identification broadcasting systems and low-altitude airspace safety supervision.

Key words: low-altitude UAV, ADS-B surveillance capacity, message reception rate, operation identification broadcast