# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 1 Introduction
[21] proposed an end-to-end deep learning network for satellite selection based on the PointNet and VoxelNet networks. However, compared with SPP that uses only pseudorange observations, the situation of constantly re-estimated integer ambiguity should be considered when the satellite selection algorithm is applied in kinematic PPP. The content of these mainstream satellite selection methods should also be understood, which is described in the next paragraph. In the maximum volume algorithm, if the volume of a tetrahedron composed of four satellites is maximal, the four satellites are selected. In general, more than four satellites are desirable in order to degrade the effect of the measurement errors and increase the robustness of estimating navigation solutions [18; 22]. Additionally, this method is time consuming due to a large number of about 24 visible satellites in multi-GNSS. For the optimal DOP algorithm, it often selects a set of satellites whose position dilution of precision (PDOP) or geometric position dilution of precision (GDOP) is minimal. However, it also has a larger computation load to a GNSS receiver. For instance, when 12 satellites are selected from 24 satellites, 2,704,156 (C\({}^{12}_{24}\)) GDOPs or PDOPs need to be computed, which causes a massive and time-consuming computation when other satellite selection methods are compared. The highest elevation angle satellite selection algorithm is the simplest and the least time consuming, but it often has poor geometries. Compared with the optimal DOP algorithm, the fast-rotating partition satellite selection algorithm based on the equal distribution of sky can greatly reduce the computation time and has a similar performance in positioning. The accuracy of different systems is also different in the process of positioning [23]. Therefore, it may be inappropriate to apply the same algorithm to the satellites of different constellations in the satellite selection solution. In this paper, we proposed a new satellite selection approach (NSS model) to reduce the excessive redundant information in GPS/BDS/GLONASS kinematic PPP with undifferenced and uncombined observations. The problem of constantly re-estimated ambiguity encountered in the application of satellite selection algorithm is considered. In addition, the positioning accuracy of the NSS model in GPS/BDS/GLOAASS kinematic PPP is also compared with that of the original PPP and the optimal DOP algorithm PPP solution. The structure of the article is as follows. Firstly, the introduction is described. Secondly, the PPP mathematical model and satellite selection algorithms are introduced. Additionally, these selection algorithms include a new elevation partition satellite selection algorithm which is inspired by the structure of the fast-rotating partition satellite selection algorithm. Thirdly, in consideration of the accuracy and characteristic in the three systems, the NSS model that applies three different algorithms for GPS, BDS, and GLONASS is proposed. It can meet the performance of kinematic positioning and reduce the amount of computation when the NSS model is used in undifferenced and uncombined kinematic PPP. The inheritance of integer ambiguity is also described to solve the problem of constantly re-estimated ambiguity in kinematic PPP. At last, the time complexity of the optimal DOP algorithm and the NSS model is compared to measure the complexity and the amount of computation. In addition, the data from five multi-GNSS Experiment (MGEX) stations on day of year (DOY) 239 in 2017 and the measured data were used to verify the model presented in this study [24]. Through this satellite selection model, the computational load of the GNSS equipment was able to be effectively reduced and the positioning accuracy and reliability of kinematic PPP were maintained at the same time, which provides some reference for the application of satellite selection algorithms in kinematic PPP.