aiqbench analysis
This research addresses the critical vulnerability of unmanned aerial systems (UAS) to Remote Identification spoofing attacks, which can lead to mid-air collisions if standard planners assume broadcast data is trustworthy. By integrating physical-layer signal strength measurements and chance-constrained optimization into a decentralized Markov decision process, the framework enables real-time detection of spoofed signals while maintaining mission scalability for multi-agent delivery scenarios. The study demonstrates that explicitly modeling RID uncertainty significantly reduces near-miss events compared to conventional approaches relying on unverified location data.
Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
arXiv:2607.19650v1 Announce Type: new Abstract: 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.
Abstract: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.
Robotics (cs.RO); Multiagent Systems (cs.MA)
arXiv:2607.19650 [cs.RO]
(or arXiv:2607.19650v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.19650 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 242 words
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