针对无人机远程识别欺骗攻击，提出可实时避障的自主轨迹规划方法。
Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
- 将远程识别数据视为不可信，结合信号强度判断欺骗并定位
- 通过概率模型构建风险区域，显著减少近距碰撞事件
- 适合需要高安全性的无人机群协同任务，如快递配送
本文提出一种针对小型无人飞行器在远程识别（RID）位置欺骗攻击下运行的去中心化、抗欺骗轨迹规划框架。现有规划器通常假设RID广播可信，一旦发生欺骗，会增加失去间隔和空中相撞的风险。本文方法将RID信息视为未验证，结合邻近飞机的物理层信号强度观测来评估广播可信度，并利用其检测欺骗行为，概率性定位欺骗源。由此产生的不确定性通过机会约束公式转化为风险受限的不安全区域，并集成到基于马尔可夫决策过程的单机规划器中。该方法支持实时、去中心化避障，同时保持任务目标和可扩展性。多架无人机包裹配送场景的仿真结果表明，相比假设RID数据真实性的规划器，本方法显著降低了近距空中碰撞事件，且计算效率满足实时执行要求。
原文摘要 · Abstract (English)
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.
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