A BDS/5G Combined Positioning Method Based on Adaptive Optimal Selection-Robust Hybrid Adaptive Kalman Filter Algorithm

Real time and highly robust localization is essential for location-based services and autonomous driving. Nevertheless, it is hard to obtain high-quality observations from these vehicle-level positioning sensors because of the uncertainty of urban environment and conditions, which affects the localization performance. In this study, we propose an adaptive optimal selection-robust hybrid adaptive Kalman filter (AOS-RHAKF) method of combination data from BeiDou navigation satellite system (BDS)/the fifth-generation (5G) network to achieve high-accuracy positioning estimation in urban complex environment. The proposed method is mainly composed of three sequential modules, namely, initial positioning estimation, AOS-based 5G base stations (BSs) measurement data optimization and BDS/5G combined positioning. Initial positioning estimation uses the raw measurement data and the basic mathematical model with position estimation to work out the mobile vehicle position. The AOS-based 5G BSs measurement data optimization module achieves better reselection of observation data through the adaptive optimal selection factor. The BDS/5G combined positioning method utilizes the optimized 5G data and BDS to establish a tightly coupled structure model, and then achieves high-precision positioning of mobile vehicles using RHAKF method. Finally, both simulations and actual driving test were carried out. The results show that the proposed AOS-RHAKF method significantly improves the positioning accuracy compared with the BDS, 5G-only, and BDS/5G loose coupling positioning using the raw measurement data.