Preliminary Performance Analysis of Tightly Coupled 5G/PPP Positioning Based on Different 5G Observations

Precise point positioning (PPP) is widely used as a high-precision positioning technology due to its accuracy and base station independence. However, it cannot meet the requirements of high-precision, high-reliability positioning in challenging urban environments due to its long convergence time and insufficient observation continuity. The fifth-generation (5G) mobile communication system has the advantages of higher frequency bands, beam-forming technology, and lower latency, which can assist PPP in addressing the issues. Accordingly, this article presents a tightly coupled 5G/PPP positioning approach based on the extended Kalman filter (EKF), which directly integrates the raw 5G observations including the angle, round-trip time (RTT), and time-difference-of-arrival (TDOA) with PPP to improve the positioning performance in urban environments. The effectiveness of the tightly coupled 5G/PPP integration system is evaluated using diverse 5G observations in the presence of white Gaussian noise. Both static and vehicle-mounted dynamic field test results demonstrate that the 5G base station signal can markedly enhance the convergence speed and positioning accuracy of PPP. The optimal localization performance among the different 5G observation combinations is achieved by the tightly coupled integration of RTT/AOD and PPP. The convergence time can be reduced to less than 1.0 min in the case of static experiments. In the vehicle-mounted dynamic tests, the CDF99.9 of the horizontal position error can be maintained at less than 1 m, the CDF55.6 at less than 0.1 m, the CDF98.7 of the vertical position error at less than 1 m, and the CDF34.2 at less than 0.1 m.