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

2026-04-09

AI Summary

This study addresses the challenge of achieving sub-meter drone localization in 5G New Radio (5G-NR) using extremely short repetition measurement sequences, where existing approaches lack efficient denoising methods compatible with the 3GPP framework. To bridge this gap, the authors propose a lightweight Adaptive Gain Exponential Smoother (AGES), which uniquely integrates 3GPP-standardized measurement quality reports into an adaptive filtering mechanism to dynamically adjust the gain of exponential weighted averaging, specifically tailored for ultra-short time-difference-of-arrival (TDoA) sequences. Requiring only 3–5 repetitions of positioning reference signals (PRS), AGES reduces localization error by 30%–40% while maintaining compatibility with existing 5G-NR infrastructure. The method effectively balances low latency and high accuracy, fulfilling the stringent safety requirements for drone operations in urban environments.

Technology Category

• Intelligent Robots: State Estimation
• Search and Optimization: Local Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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.

Problem

• UAV localization
• TDoA denoising
• 5G-NR
• ultra-short measurements
• sub-meter accuracy

Innovation

• Adaptive Gain Exponential Smoother
• TDoA denoising
• 5G-NR UAV localization
• measurement quality-aware filtering
• ultra-short measurement sequences