多智能体强化学习实现无人机协同搜寻跟踪，提升关键设施反制能力
Cooperative Search and Track of Rogue Drones using Multiagent Reinforcement Learning
- 设计多智能体强化学习算法，协调无人机团队搜索与追踪
- 在多场景模拟中实现对多个目标无人机的高效检测与持续跟踪
- 适用于军事或安保场景的反无人机系统开发
本文研究针对敏感设施的恶意无人机拦截问题。现有技术多聚焦于干扰或欺骗，但对有效定位与追踪缺乏关注。本工作提出由多个追捕型无人机组成的团队，在敏感设施上空协同搜索、发现并持续追踪多个恶意无人机。通过新型多智能体强化学习框架优化各智能体的移动控制策略，以最大化被探测和跟踪的恶意无人机数量。在不同规模智能体配置下进行大量仿真测试，验证了该系统的性能与可扩展性。
原文摘要 · Abstract (English)
This work considers the problem of intercepting rogue drones targeting sensitive critical infrastructure facilities. While current interception technologies focus mainly on the jamming/spoofing tasks, the challenges of effectively locating and tracking rogue drones have not received adequate attention. Solving this problem and integrating with recently proposed interception techniques will enable a holistic system that can reliably detect, track, and neutralize rogue drones. Specifically, this work considers a team of pursuer UAVs that can search, detect, and track multiple rogue drones over a sensitive facility. The joint search and track problem is addressed through a novel multiagent reinforcement learning scheme to optimize the agent mobility control actions that maximize the number of rogue drones detected and tracked. The performance of the proposed system is investigated under realistic settings through extensive simulation experiments with varying number of agents demonstrating both its performance and scalability.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。