A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method

Abstract

In recent years, with the vigorous construction of 5G networks, the high-density deployment, low delay, and high bandwidth of 5G network systems have enabled high-precision positioning services. By integrating the BeiDou Navigation Satellite System (BDS) and 5G systems, more reliable positioning services can be provided, and BDS–5G signal integrated positioning has become a new research hotspot. However, BDS–5G signal fusion positioning faces the problems of how to build an effective fusion positioning model between heterogeneous systems and the high complexity of multiobjective function positioning solutions. Therefore, this paper constructs a TOA/TDOA fusion positioning model of BDS–5G signals and introduces the multiobjective particle swarm optimization positioning solution method (MOPSO) to realize the reliable positioning of BDS and 5G signal system fusion (MOPSO-TOA/TDOA). The experimental results show that compared with the traditional BDS–5G signal fusion positioning algorithm based on a Kalman filter, the positioning accuracy of the BDS–5G signal joint solution system based on a multiobjective particle fusion algorithm is 24.8% higher than that of the Kalman filter in terms of positioning convergence time, 18.9% higher in terms of system positioning accuracy, and 50–80% higher than that of the BDS positioning system alone, and the reliable positioning ability in complex environments is effectively enhanced.