Quality-Aware Denoising of Ultra-Short TDoA Measurements for 5G-NR UAV Localization
For UAVs in safety-critical urban operations, it improves positioning accuracy under stringent latency constraints within existing 3GPP frameworks.
The paper tackles UAV localization in 5G-NR networks, proposing AGES to denoise ultra-short TDoA measurements. It achieves 30-40% reduction in positioning error with only 3-5 repeated measurements.
Reliable positioning is essential for Uncrewed Aerial Vehicles (UAVs) in safety-critical urban operations, yet achieving sub-meter accuracy under stringent latency constraints remains challenging. While 3rd Generation Partnership Project (3GPP) specifies repeated Positioning Reference Signals (PRS) transmissions for accurate Time Difference of Arrival (TDoA) measurements, denoising techniques specifically tailored for extremely limited measurement sequences within 3GPP frameworks remain underexplored. We propose Adaptive Gain Exponential Smoother (AGES), a lightweight filter combining exponentially weighted averaging with adaptive gains informed by 3GPP measurement quality reports. Simulations demonstrate AGES achieves 30-40% reduction in positioning error with only 3-5 repeated measurements while maintaining Fifth Generation New Radio (5G-NR) infrastructure compatibility.