小型无人机系统的远程ID欺骗感知轨迹规划
Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
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中文总结 AI 辅助
针对小型无人机在RID位置欺骗攻击下的轨迹规划问题，提出分散式欺骗感知框架，利用物理层观测评估广播可信度，检测定位欺骗代理，集成到规划器中，仿真显示可减少近空中碰撞事件且保持计算效率。
中文摘要 AI 辅助
本文提出了一种用于小型无人机系统的分散式、欺骗感知轨迹规划框架，该系统在远程识别（RID）位置欺骗攻击下运行。现有规划器通常假定RID广播是可信的，欺骗发生时会增加失去间隔和空中碰撞风险。相比之下，该方法明确将RID信息视为未经验证的，并纳入物理层观测以评估广播可信度。利用来自相邻飞机的接收信号强度测量来检测欺骗并概率性地定位欺骗代理。通过机会约束公式将结果不确定性转换为风险受限的不安全区域，并集成到基于每个代理的马尔可夫决策过程的规划器中。这实现了实时、分散式碰撞避免，同时保留任务目标和可扩展性。在多架飞机包裹递送场景中的仿真结果表明，与假定RID数据真实的规划器相比，近空中碰撞事件减少，同时保持适合实时执行的计算效率。
英文摘要
This work presents a decentralized, spoofing-aware trajectory planning framework for small unmanned aerial systems operating under Remote Identification (RID) location spoofing attacks. Existing planners typically assume RID broadcasts are trustworthy, which can increase the risk of loss of separation and mid-air collisions when spoofing occurs. In contrast, the proposed approach explicitly treats RID information as unverified and incorporates physical-layer observations to assess broadcast credibility. Received signal-strength measurements from neighboring aircraft are used to detect spoofing and probabilistically localize a spoofing agent. The resulting uncertainty is converted into a risk-bounded unsafe region using a chance-constrained formulation and integrated into a per-agent Markov decision process-based planner. This enables real-time, decentralized collision avoidance while preserving mission objectives and scalability. Simulation results in a multi-aircraft package delivery scenario demonstrate reduced near mid-air collision events compared to planners that assume truthful RID data, while maintaining computational efficiency suitable for real-time execution.
