A Crossing Azimuth based Optimal Base Station Selection Algorithm for 5G Positioning

We propose an optimal selection method for 5G base station data to achieve high-accuracy positioning estimation in indoor and outdoor environments. The proposed method is mainly composed of three sequential modules, namely initial positioning estimation, measurement data optimization and estimated position update. Initial positioning estimation uses the raw measurement data and the basic mathematical model with position estimation to work out the mobile vehicle position. The measurement data optimization module takes the number of buildings in the azimuth threshold area set between the initial position of the mobile vehicle and the 5G base station position as the judgment condition to achieve the re-selection of data. The estimated position update implements the initial estimated position update of the mobile vehicle by using the preferred data and the basic mathematical model with pose estimation. The results of the simulations show that the performance of the localization results of the solution mode with preferred data is significantly better than that of the solution mode using the initial data measurement.