Remote ID Spoofing-Aware Trajectory Planning for Small Unmanned Aerial Systems

2026-07-21

AI Summary

This work addresses the collision risk faced by small unmanned aircraft systems due to position spoofing attacks exploiting untrusted Remote ID (RID) broadcasts. To mitigate this vulnerability, the authors propose a decentralized trajectory planning framework that treats RID data as unverified and fuses it with physical-layer received signal strength measurements. This integration enables probabilistic localization to estimate potential spoofing sources and construct risk-bounded unsafe regions, which are then incorporated into a real-time Markov decision process–based planner. The approach achieves, for the first time, joint optimization of RID spoofing detection and risk-constrained trajectory planning. It significantly reduces near-miss incidents in multi-agent logistics scenarios while preserving mission performance, system scalability, and computational efficiency suitable for real-time operation.

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.

Problem

• Remote ID spoofing
• trajectory planning
• small unmanned aerial systems
• collision avoidance
• decentralized planning

Innovation

• Remote ID spoofing
• trajectory planning
• decentralized collision avoidance
• chance-constrained optimization
• signal-strength-based localization