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

TL;DR

The paper tackles GNSS-denied terrestrial positioning by leveraging 5G infrastructure and carrier phase ranging. It introduces a multi-frequency CPP approach with virtual wavelength processing to achieve cm-level accuracy under LOS to at least three BSs, while deploying a DL LOS/NLOS classifier to filter unreliable links and a sensor-fusion ES-EKF to maintain tracking when LOS fails. The system is evaluated via 3GPP UMi-based simulations and KITTI urban data, showing sub-5 meter positioning error in LOS scenarios and robust continuity through NLOS conditions thanks to VO/IMU-CPP fusion. This work demonstrates the practicality of 5G-based localization as a resilient alternative to GNSS, with implications for autonomous driving and industrial automation in urban environments.

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.

Figures (16)

• Figure 1: System-level depiction of the designed positioning and tracking system. The focused areas of the paper contributions, along with the relevant sections, are also shown.
• Figure 2: Simulated RF coverage in a suburban environment using four BSs. The map shows path gain levels in dB, calculated via ray-tracing including LOS, reflection, and diffraction effects.
• Figure 3: Compares two network planning strategies: (1) The four blue cake-shaped symbols represent a conventional deployment designed primarily for communication services, resulting in limited LOS coverage—blue grid cells indicate regions with LOS to at least three BSs. (2) In contrast, the twelve black cake-shaped symbols correspond to a network layout optimized for localization, where positioning is treated as a primary service.
• Figure 4: Example SRS configuration within the NR OFDMA grid. SRS allocation within a single PRB, illustrating comb-based subcarrier mapping across OFDM symbols.
• Figure 5: Phase spectrum at the transmitter and receiver, showing the phase shifts resulting from the propagation and communication channel.