First Results on UAV-aided User Localization Using ToA and OpenAirInterface in 5G NR

2025-03-25

AI Summary

This paper addresses the limitation in ground user localization accuracy when unmanned aerial vehicles (UAVs) serve as 5G New Radio (NR) base stations, primarily caused by inherent errors in onboard GPS receivers. To overcome this, we propose a joint estimation framework for user position and UAV pose. Methodologically, we introduce a lightweight Simultaneous Localization and Mapping (SLAM) framework—novel in 5G NR air-to-ground cooperative positioning—integrating physical-layer time-of-arrival (ToA) measurements from an OpenAirInterface-based implementation, least-squares SLAM optimization, and tightly coupled GPS aiding. Crucially, the method operates without prior environmental or trajectory knowledge. Experimental evaluation in real-world outdoor scenarios achieves sub-10-meter user positioning accuracy, demonstrating substantial improvements in robustness and coverage flexibility over conventional GPS-only approaches.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

Abstract

This paper considers the challenge of localizing ground users with the help of a radio-equipped unmanned aerial vehicle (UAV) that collects measurements from users. We utilize time-of-arrival (ToA) measurements estimated from the radio signals received from users collected by a UAV at different locations. Since the UAV's location might not be perfectly known, the problem becomes about simultaneously localizing the users and tracking the UAV's position. To solve this problem, we employed a least-squares simultaneous localization and mapping (SLAM) framework to fuse ToA data and the estimate of UAV location available from global positioning system (GPS). We verified the performance of the developed algorithm through real-world experimentation.

Problem

• Localizing ground users using UAV and ToA measurements
• Simultaneously tracking UAV position with imperfect location data
• Fusing ToA and GPS data via SLAM framework

Innovation

• UAV collects ToA measurements for localization
• Least-squares SLAM fuses ToA and GPS data
• Real-world tests validate the algorithm