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

TL;DR

This paper introduces a 5G-based terrestrial positioning system that fuses sensor data and employs deep learning to achieve accurate localization in GNSS-denied urban environments, demonstrating less than 5 meters error.

Contribution

It proposes a novel hybrid localization approach combining carrier phase ranging, deep learning link classification, and sensor fusion to improve accuracy and robustness in obstructed scenarios.

Findings

Achieves less than 5 meters positioning error in urban environments.

Uses deep learning to identify NLOS links and improve trilateration.

Demonstrates robustness through sensor fusion with IMUs and cameras.

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.