[1]裴佳明,孔伟力,于长东,等.基于多无人机协作与联邦学习的目标检测与跟踪系统研究[J].智能系统学报,2025,20(5):1158-1166.[doi:10.11992/tis.202412031]
PEI Jiaming,KONG Weili,YU Changdong,et al.A multi-UAV collaborative system and federated learning for target detection and tracking based on federated learning[J].CAAI Transactions on Intelligent Systems,2025,20(5):1158-1166.[doi:10.11992/tis.202412031]
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PEI Jiaming,KONG Weili,YU Changdong,et al.A multi-UAV collaborative system and federated learning for target detection and tracking based on federated learning[J].CAAI Transactions on Intelligent Systems,2025,20(5):1158-1166.[doi:10.11992/tis.202412031]
基于多无人机协作与联邦学习的目标检测与跟踪系统研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第5期
页码:
1158-1166
栏目:
学术论文—机器感知与模式识别
出版日期:
2025-09-05
- Title:
- A multi-UAV collaborative system and federated learning for target detection and tracking based on federated learning
- Keywords:
- unmanned aerial vehicle; federated learning; target detection; communication; collaborative multi-UAV system; target tracking; cooperative system; coordination algorithm; neural network
- 分类号:
- TP319
- 摘要:
- 本文提出了一种多无人机协作系统，旨在在各种环境中实现高效且可靠的目标检测与跟踪。该系统利用先进的协调算法和联邦学习技术来提升性能，确保无人机之间的高覆盖率、低冗余度和有效的任务分配。通过大量仿真实验和实证实验验证了系统在简单与复杂场景(如开阔地与密集的城市区域、夜间与雨天等挑战性条件下)的强大性能。文章使用覆盖率、冗余率、任务分配均衡性、响应时间和跟踪连续性等关键指标来评估系统的有效性。结果表明，系统在较简单的环境中表现优异，同时在更具挑战性的条件下也能保持稳健的性能，但仍存在进一步优化的空间。本文最后讨论了系统的部署策略以及未来工作的方向，特别是在动态和GPS信号缺失环境下提高系统的适应性。
- Abstract:
- This paper presents a multi-UAV collaborative system designed to achieve efficient and reliable target detection and tracking. The system employs and federated learning techniques to ensure balanced task allocation across different operational environments with high coverage rate, low redundancy and high efficiency. Extensive simulations and real-world experiments were conducted to evaluate the system’s performance in various scenarios, including open fields and complex urban areas under challenging conditions such as nighttime and rainy weather. Key metrics such as coverage rate, redundancy rate, task allocation balance, response time, and tracking continuity were used to assess the system’s effectiveness. The results demonstrate that while the system excels in simpler environments, it maintains robust performance in more demanding conditions, highlighting areas for further optimization. The paper concludes with discussions on deployment strategies and future research directions, particularly focusing on enhancing system adaptability in dynamic and GPS-denied environments.
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备注/Memo
收稿日期:2024-12-26。
基金项目:国家自然科学基金青年科学基金项目(52401362).
作者简介:裴佳明，博士研究生，主要研究方向为联邦学习、分布式系统。发表学术论文40余篇。E-mail：jiamingpei0262@ieee.org。;于长东，讲师，博士，主要研究方向为机器学习、计算视觉、流体智能感知和海上无人系统决策与控制技术，主持国家自然科学基金青年科学基金项目1项。E-mail：ycd@dlmu.edu.cn。;王鲁昆，副教授，主要研究方向为机器学习、计算视觉、流体智能感知和海上无人系统决策与控制技术。主持和参与国家自然科学基金项目、山东省自然科学基金项目、泰安市科技计划项目、高校科技计划项目等项目10项，以及教育部协同育人项目3项。获发明专利授权11项、软件著作权12项，发表学术论文21篇。 E-mail：wanglukun@sdust.edu.cn。
通讯作者:王鲁昆. E-mail：wanglukun@sdust.edu.cn
更新日期/Last Update:
2025-09-05