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.

翻译：本文提出一种基于5G基础设施的地面定位系统，作为全球导航卫星系统（GNSS）的有效替代方案，尤其适用于GNSS信号受阻或不可用的场景。与传统地面网络侧重通信服务不同，本文探讨了以定位为核心业务的网络规划方案。基于面向定位优化的网络架构，本文提出一种融合载波相位（CP）测距与三边定位技术的系统：当至少三个基站（BS）提供视距（LOS）传输条件时，即可实现用户定位。实现高精度CP定位需解决三大挑战：整周模糊度解算、视距/非视距（LOS/NLOS）链路识别，以及遮挡条件下的定位。对此，系统采用多载波CP方法，无需显式估计整周模糊度；同时开发深度学习模型识别NLOS链路并将其排除出三边定位过程。当LOS路径被遮挡导致CP测距失效时，系统引入误差状态扩展卡尔曼滤波器（error-state EKF）融合惯性测量单元（IMU）和摄像头等辅助传感器数据。这种混合策略能够在复杂信道条件下实现对移动用户的鲁棒跟踪。采用真实场景KITTI数据集对系统性能进行验证，包含城市环境下移动车辆的定位测试。仿真结果表明，在KITTI城市场景下，该系统定位误差小于5米，性能与商用公共GNSS服务相当，充分展现了其在GNSS拒止环境中作为鲁棒精准定位方案的潜力。