# A New Hybrid Positioning Method by Fusion of BDS and 5G Signal Using the Particle Swarm Method
## 1 Introduction
It used channel deviation distribution estimation (H-BLADE) to integrate the time difference of arrival (TDOA) measurements of GNSS and LTE. It improved the location performance in harsh environments, such as urban canyons and indoor environments, through cellular location systems. [PERSON] et al. (2019) [14] presented a representative method to simulate and evaluate the hybrid positioning capabilities of GNSS and 5G networks in outdoor urban, suburban, and rural scenes. A new scenario definition was proposed that combined the network density of 5G deployment with the visibility mask of GNSS satellites. [PERSON] et al. (2018) [15] proposed a new 5G-GNSS hybrid positioning scheme that combined the angle of arrival (AOA) estimates from 5G base stations and time of arrival (TOA) measurements from GNSS satellites. The TOA measurement value of 5G base stations was not used to avoid the time synchronization error of 5G networks. In [16], a multirate adaptive Kalman filter was proposed to solve the problem of nonstationarity of 5G measurement noise in GNSS-5G hybrid positioning. However, the above GNSS-5G hybrid positioning methods were based on Newton iterative LS (least square) or Kalman filter positioning methods. When there were few visible satellites and base stations, the positioning results were difficult to converge, the convergence time was too long to locate in real-time, and the positioning accuracy was poor [17; 18; 19; 20; 21]. Aiming at the problem of multisource fusion, [PERSON] et al. (2021) [22] proposed a new data heterogeneous fusion method by using improved particle filter fusion technology to optimize the model for the problem of multisource heterogeneous fusion of GNSS and environmental data in landslides and other environments. Aiming at the problems of slow convergence speed of data fusion in the network, sensitivity to initial values, and ease of falling into local optimal solutions, [PERSON] et al. (2020) [23] proposed a BP neural network WSN data fusion algorithm based on improved particle swarm optimization, which effectively improved the accuracy and convergence speed of data fusion. The particle swarm optimization algorithm is an effective method to solve the problem of data fusion optimization. The particle swarm optimization algorithm is widely used in multisource data fusion [24; 25; 26; 27; 28; 29]. This paper, starting with integrating BDS and the 5G signal layer, proposes a joint positioning model (MOPSO-TOA/TDOA) of BDS and 5G TOA/TDOA. Aiming at the difficulty of solving the multiobjective function in the colocalization model, we use the joint solution method of multiobjective particle swarm (MPSO), and the general algorithm flow is shown in Figure 1. The overall algorithm is divided into two parts. One is the model building part, in which the TOA data of the BDS satellite signal received by the receiver and the TDOA data of the 5G base station are used to build the system model. The second part is the model solution part, which converts multiple observation equations into multiple objective cost functions. The multiobjective particle filter algorithm is used to solve the multiobjective cost functions. The optimal solution satisfying the minimum overall function is the optimal positioning result. To verify the effectiveness of this algorithm, this paper first analyzed the convergence of the algorithm. Then, we compared the convergence and positioning accuracy of the BeiDou-5G multitarget particle fusion algorithm and Kalman filter fusion algorithm in the actual scene. Finally, we selected several scenes with the poor positioning effect of single BeiDou to carry out joint positioning experiments, including real environments, such as buildings on one side, under bridges, and between buildings. By analyzing the geometric distribution of base stations and satellites and the positioning accuracy of the joint solution, the results show that the joint solution algorithm proposed in this paper can effectively improve the reliability of positioning in occluded scenes.