Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
This paper presents a decentralized, spoofing-aware trajectory planning framework for small unmanned aerial systems under Remote Identification (RID) location spoofing attacks. Unlike prior work that assumes RID is trustworthy, the proposed approach treats RID as unverified and uses received signal strength measurements to detect spoofing and probabilistically localize the attacker. The resulting uncertainty is converted into a risk-bounded unsafe region via chance constraints and integrated into a per-agent Markov decision process planner. Simulations in a multi-aircraft package delivery scenario demonstrate reduced near mid-air collision events while maintaining computational efficiency.
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[Submitted on 22 Jul 2026]
Title:Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems
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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.
Comments: 9 pages, 3 figures, to be published in IEEE DASC 2026 Conference proceedings
Subjects:
Robotics (cs.RO); Multiagent Systems (cs.MA)
Cite as: arXiv:2607.19650 [cs.RO]
(or arXiv:2607.19650v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.19650
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jeremiah Webb [view email] [v1] Wed, 22 Jul 2026 01:16:52 UTC (663 KB)
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