# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 5 Conclusions  
The significant increase in operating satellites in view turns out not only a benefit for the positioning accuracy but also a challenge to the processing capability of the general civil low-cost navigation device, as well as the channel limitation of the GNSS receiver processor. This study investigates the importance of satellite selection in the multi-constellation navigation system then proposed a performance-requirement-driven fast satellite selection algorithm in order to find a relatively small subset that meets the requirements for the RAIM applications.  
The investigations about subset size impacts on the accuracy, integrity, and computation load show that, it may be not the more selected satellites the better for the RAIM process and satellite selection is quite effective when real-time requirement and channel capabilities are considered. The accuracy impact of a single satellite can be represented by its slope on the current subset while the integrity impact is indicated by the max slope value it reduces. A cost function based on the performance requirement can help to select a valuable satellite for the current subset. A performance-requirement-driven fast satellite selection algorithm is raised by geometric evenly selecting the initial subset and adding valuable satellites iteratively to the initial subset until the performance requirement is achieved. The proposed algorithm keeps a similar accuracy performance and an about 15 m smaller horizontal protection level than the geometric algorithm and the downdate algorithm in the 12-satellite subset trial and can achieve an average 1.0 to 2.0 satellites smaller subset than the other two algorithms under the given LNAV and APV-I horizontal  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Fixed Subset Size Performance**} & \multicolumn{2}{c}{**Minimal Subset Size Performance**} \\ \cline{2-5}
**Algorithms** & **2 drms (m)** & **HPL (m)** & **LNAV** & **APV-I** \\ \hline Geometric algorithm & 15.3246 & 65.1377 & 8.1250 & 15.5417 \\ Downdate algorithm & 13.2208 & 53.2312 & 8.1458 & 13.9792 \\ Proposed algorithm & 11.9713 & 37.9720 & 6.8125 & 11.9167 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison among the three algorithms in high latitudes.  
requirements, regardless of the latitude of the receiver. Consequently, it could be a good choice for real-time RAIM applications and low-cost navigation devices.  
Conceptualization, H.W.; Methodology, S.L. and Z.L.; Supervision, Y.C. and C.C.; Writing--original draft, H.W.; Writing--review and editing, Y.C., C.C., S.L. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61901380 and in part by the Stable Supporting Fund of National Key Laboratory on Blind Signal Processing under Grant 61424131903.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.