# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 6 Conclusions  
In this paper, an indoor pseudolite CSI fingerprinting method based on ray tracing and artificial neural network is proposed. According to the positioning requirements of large indoor scenes, the method uses the characteristic parameters generated by deterministic modeling to expand the input data set of an artificial neural network. The test environment is built in the arrival hall of an airport, and the positioning performance of the method has been tested and verified using the pseudolite positioning terminal developed by the State Key Laboratory of Satellite Navigation System and Equipment. The results show that the positioning accuracy is improved in the area where fingerprints have been actually collected, preliminary positioning can also be completed in the area without fingerprints collection.  
Our current work is still rather preliminary at this stage, mostly due to the fact that the characteristics of simulated and measured CSI data are not sufficiently diverse. In future work, we intend to propose and analyze more CSI features such as AOA observations and Doppler observations. We also intend to apply this approach to dynamic localization testing to improve pedestrian localization accuracy in large venues.  
All authors contributed to the manuscript and discussed the results. All authors together developed the idea that led to this paper. [PERSON] and [PERSON] conceived the experiments and analyzed the data. [PERSON] provided critical comments and contributed to the final revision of the paper. [PERSON] contributed to the expression and the design of programs. [PERSON] wrote the manuscript and all the authors participated in amending the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Plan of China (project: General and application verification of large-scale underground scalable PNT system: No. 2021 YFB3900801) and Foundation of Technological Innovation Guidance Plan of Hebei (project: Research and application of key technologies of Beidou micro base station indoor hybrid positioning system for the Beijing Winter Olympics: No. 2147/603D).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c|c c c c c} \hline \hline \(\mathbf{y}\)\(\mathbf{x}\) & **1** & **2** & **3** & **4** & **5** \\ \hline
1 & (1.2377, 0.2349) & (0.6923, 1.0887) & (1.6501, 0.2219) & (3.7950, 0.1302) & (5.6715, 1.9023) \\
2 & (2.8339, 1.8230) & (1.5664, 2.6458) & (3.0349, 2.2396) & (3.8759, 3.0136) & (3.7925, 1.3151) \\
3 & (0.0412, 2.4235) & (2.3426, 3.7335) & (3.7254, 2.4245) & (5.4897, 2.365) & (5.7172, 3.2848) \\
4 & (1.8622, 3.5277) & (2.1784, 3.5315) & (2.9369, 3.7992) & (4.4090, 4.6586) & (5.6302, 6.1836) \\
5 & (1.3188, 4.3277) & (2.7694, 6.0543) & (3.7147, 3.8620) & (5.4172, 5.6108) & (5.4889, 6.1659) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Positioning results of uncollected fingerprint area.  
Figure 14: Positioning results of the area without collected fingerprints.