Observation Filtering Dramatically Improves Drone Collision Avoidance Under GNSS Degradation

Jul 14, 2026

A new preprint evaluates two runtime safety architectures—action filtering and observation filtering—for learned small unmanned aircraft system (sUAS) separation policies operating under degraded GNSS conditions. The study finds that observation filtering, which presents a worst-case state estimate to the policy, reduces near mid-air collisions by 90%, while action filtering, which overrides policy outputs with hand-designed constraints, offers negligible improvement. The results indicate that maintaining the policy's decision authority is more effective for safety than externally constraining its actions.

Why it matters: This work provides important guidance for designing safer autonomous drone systems in environments with unreliable GNSS signals, a key challenge for real-world deployment.

Full story at: arXiv Multiagent Systems ↗