A Robust 5G Terrestrial Positioning System with Sensor Fusion in GNSS-denied Scenarios

2025-07-22

AI Summary

To address the need for high-precision terrestrial positioning under GNSS-denied conditions, this paper proposes a collaborative 5G-based positioning system leveraging terrestrial infrastructure. Methodologically: (i) a multi-carrier carrier-phase ranging scheme is designed to circumvent integer ambiguity resolution; (ii) a deep learning model is introduced to classify non-line-of-sight (NLoS) propagation links; and (iii) when line-of-sight is obstructed, an error-state extended Kalman filter (ES-EKF) tightly fuses IMU, monocular camera, and 5G ranging measurements. The key contribution is a GNSS-free, end-to-end robust positioning framework. Evaluated on the KITTI urban dataset, it achieves a mean positioning error of less than 5 meters—comparable to commercial GNSS performance—while significantly improving continuity and reliability in complex urban environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a terrestrial localization system based on 5G infrastructure as a viable alternative to GNSS, particularly in scenarios where GNSS signals are obstructed or unavailable. It discusses network planning aimed at enabling positioning as a primary service, in contrast to the traditional focus on communication services in terrestrial networks. Building on a network infrastructure optimized for positioning, the paper proposes a system that leverages carrier phase (CP) ranging in combination with trilateration to localize the user within the network when at least three base stations (BSs) provide line-of-sight (LOS) conditions. Achieving accurate CP-based positioning requires addressing three key challenges: integer ambiguity resolution, LOS/NLOS link identification, and localization under obstructed LOS conditions. To this end, the system employs a multi-carrier CP approach, which eliminates the need for explicit integer ambiguity estimation. Additionally, a deep learning model is developed to identify NLOS links and exclude them from the trilateration process. In cases where LOS is obstructed and CP ranging becomes unreliable, the system incorporates an error-state extended Kalman filter to fuse complementary data from other sensors, such as inertial measurement units (IMUs) and cameras. This hybrid approach enables robust tracking of moving users across diverse channel conditions. The performance of the proposed terrestrial positioning system is evaluated using the real-world KITTI dataset, featuring a moving vehicle in an urban environment. Simulation results show that the system can achieve a positioning error of less than 5 meters in the KITTI urban scenario--comparable to that of public commercial GNSS services--highlighting its potential as a resilient and accurate solution for GNSS-denied environments.

Problem

• Develops 5G-based positioning for GNSS-denied environments
• Addresses challenges in carrier phase ranging and LOS/NLOS identification
• Integrates sensor fusion for robust tracking in obstructed conditions

Innovation

• Uses 5G carrier phase ranging with trilateration
• Employs deep learning for NLOS link identification
• Fuses sensor data with Kalman filter for robustness