# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 1 Introduction  
The BeiDou satellite navigation system (BDS) is a Global Satellite Navigation Satellite System (GNSS) independently developed and operated by China. By receiving satellite signals, the BeiDou satellite positioning receiver can obtain pseudorange (the measured distance between the satellite and the receiver) and ephemeris data. The satellite's real-time position can be solved through the satellite ephemeris. Due to the existence of a satellite clock error, we need at least four observable satellites to calculate the exact position of the receiver. However, in a complex urban environment, there is the problem that buildings block visible satellites, resulting in poor satellite geometry. At the same time, buildings on both sides of a roadblock will also lead to poor signal continuity. These problems will seriously affect the performance of the satellite navigation system, resulting in reduced positioning accuracy or even an inability to locate [1; 2; 3; 4; 5]. The 5G system has the characteristics of large bandwidth, dense networking, and multiple antennas and can achieve high-precision positioning under indoor sheltered conditions. With the maturity of 5G-related systems and the development of high-precision time synchronization technology, the application of 5G technology in life is becoming increasingly important. The 5G system is characterized by large bandwidth, dense networking, and multiple antennas. The maximum distance between 5G base station sites is less than 200 m, characterized by ultra-dense heterogeneous networks. Considering the 5G base station signal during satellitepositioning can supplement the geometric configuration of satellite positioning and reduce the system position accuracy factor value (PDOP, the smaller the value, the higher the positioning accuracy). Therefore, the 5G base station signal can effectively improve the number of visible satellites and the positioning performance of the system in complex environments [6; 7; 8]. The high-precision positioning technology of BeiDou-5G fusion based on 5G signals has increasingly become the focus of academic and industrial circles. Many scholars have studied the integration and positioning of GNSS and 5G. [PERSON] et al. (2017) [9] combined the network density of 5G deployment with the visibility of GNSS satellites. Studying and establishing the model of 5G network deployment and satellite visibility was helpful to achieve better positioning switching. However, it is still necessary to further study the design of 5G networks and consider accurate vehicle positioning in the case of multipaths. [PERSON] et al. (2021) [10] studied GNSS equipment failure and skyscraper blocking in bad weather and proposed a GNSS-free emergency location service based on 5G internet of vehicles using fog computing. Through simulation verification, this paper preliminarily discusses the fog computing architecture design of emergency location services. [PERSON] et al. (2018) [11] studied theoretically deriving the Fisher information matrix (FIM) and the lower limit of position and rotation errors of 5G-GNSS hybrid positioning estimation. Then, they used a two-step method, first calculating the FIM of position-related parameters and then calculating the rotation equivalent Fisher information matrix (EFIM) of position information for fusion positioning, which improved the positioning accuracy to a certain extent and reduced the number of satellites needed. [PERSON] et al. (2018) [12] studied fusing the positioning results of cellular networks and GNSS through an extended Kalman filter (EKF) or particle filter to filter the observation noise of cellular networks. However, the disadvantage is that if the terminal was close to the base station, the performance gain was not very high, and the improvement of satellite positioning accuracy was limited. [PERSON] et al. (2018) [13] proposed a location algorithm that integrated GNSS data and 4G long-term evolution (LTE) system data.