# A CSI Fingerprint Method for Indoor Pseudolite Positioning Based on RT-ANN
## 5 Result
### The Location Result of the Area with Fingerprints Collection  
The positioning data results of the two experiments are shown in Table 2. Figure 10 shows the test result of neural network positioning without simulation fingerprint data, the root mean square positioning error is 1.0696 m, Figure 11 shows the test result of neural network positioning with simulation fingerprint data, the root mean square positioning error is 0.4850 m. By comparing the results qualitatively and quantitatively, it can be seen that the improved artificial neural network adding deterministic simulation input features reduces the positioning test error (the distance between the real position and the positioning result is shortened), and the root means square error is reduced by 54.7%.  
Figure 8: Simulation result of RSS.  
Figure 9: Simulation result of TOA.  
To compare the performance of different algorithms, we choose the commonly used KNN and SVM methods in fingerprint localization, and methods in [10; 13] to compare with our method. The KNN and SVM model can be directly invoked through the deep learning framework Keras library. Similarly, the positioning calculation is performed at the 25 position reference points of test area 1 and compared with the true value points measured by the total station. Figure 12 is the positioning straight line distance error, and Figure 13 is the cumulative distribution function of the error. It can be seen that the root mean square (RMS) error of our method is 0.4850 m and the maximum error is 1.06 m, of which 92% are better than 1 m. Comparative experiments show that our method has higher localization accuracy than other methods, the detailed error analysis is shown in Table 3.  
\begin{table}
\begin{tabular}{c c|c c c c c} \hline \hline \multirow{2}{*}{**y**} & \multirow{2}{*}{**x**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} \\ \cline{3-3} \cline{5-8}  & & & & & & & \\ \hline \multirow{4}{*}{1} & before & (1.9830, 0.2349) & (2.1571, 1.0887) & (1.8036, 0.2219) & (5.0260, 0.1302) & (4.0807, 0.2075) \\  & after & (0.9059, 1.2780) & (1.7449, 1.2948) & (3.2997, 1.1568) & (4.470, 1.1110) & (4.8133, 1.3478) \\  & before & (1.6671, 1.8230) & (2.0203, 2.6458) & (3.1488, 1.7992) & (5.0661, 1.8178) & (4.6179, 1.0020) \\  & after & (1.1044, 1.9267) & (1.8790, 1.5387) & (2.7085, 1.7319) & (4.0649, 1.8837) & (4.8270, 2.0046) \\  & before & (1.6171, 2.0356) & (1.6278, 3.3335) & (5.0012, 3.5755) & (4.9678, 3.6350) & (3.8882, 3.2848) \\
3 & after & (0.5948, 2.780) & (1.7703, 4.000) & (3.3963, 3.1220) & (3.7177, 2.5306) & (5.3400, 2.5079) \\  & before & (0.7725, 5.277) & (2.4902, 4.9436) & (3.1220, 4.9431) & (4.2999, 4.0586) & (5.0921, 2.8164) \\  & after & (0.8360, 3.8335) & (1.7156, 3.8277) & (3.4257, 3.5751) & (5.0010, 4.3576) & (4.9925, 4.4191) \\  & before & (1.4846, 5.6723) & (1.1861, 3.9457) & (3.4669, 3.8620) & (5.0229, 5.6108) & (5.5693, 5.5765) \\
5 & after & (0.6448, 4.8669) & (2.1337, 4.7858) & (3.0364, 5.4668) & (4.3620, 5.1035) & (4.5519, 4.9107) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positioning results of collected fingerprint area.  
Figure 11: ANN test results with deterministic simulation input.  
Figure 10: ANN test results without deterministic simulation input.