Quality-Aware Denoising of Ultra-Short TDoA Measurements for 5G-NR UAV Localization

TL;DR

This paper introduces AGES, a lightweight denoising filter for ultra-short TDoA measurements in 5G-NR UAV localization, achieving significant accuracy improvements with minimal measurements.

Contribution

The paper presents AGES, a novel adaptive smoothing method tailored for limited measurement sequences in 5G-based UAV positioning.

Findings

AGES reduces positioning error by 30-40% with 3-5 measurements

The method maintains compatibility with 5G-NR infrastructure

Simulations validate the effectiveness of AGES in urban UAV scenarios

Abstract

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.