## 5 Experiment
### Experimental Verification and Analysis  
#### 5.2.1 Frequent Interruption Test  
To validate the algorithm's performance in common GNSS interruption environments, we designed a vehicle driving route that covers typical urban occlusion environments, including high-rise buildings, overpasses, and densely built areas. These environments frequently cause satellite signal interruptions, making it very challenging to achieve high-precision GNSS/INS integrated navigation. The test route is divided into sections with frequent GNSS interruptions, including winding roads and long straight roads. The total GNSS was interrupted 25 times during the journey. The winding road section is 2.97 km long and lasts 580 s (0-580 s), with GNSS being interrupted 16 times. The long straight road section is 4.82 km long and lasts 420 s (580-1000 s), with GNSS being interrupted 9 times. The GNSS interruption times are detailed in Table 2.  
The trajectory calculations for the section with frequent interruptions on winding roads are shown in Figure 8, the long straight section with frequent interruptions, as shown in Figure 9. Regarding the the section with frequent interruptions on winding roads, when examining the eastward error curve in Figure 10 and the northward error curve in Figure 11 (0-580 s), it is clear that all three methods exhibit a notable increase in errors in this section, with frequent GNSS interruptions compared to the uninterrupted sections.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Start Time (s)** & **Interruption Duration (s)** & **Start Time (s)** & **Interruption Duration (s)** & **Start Time (s)** & **Interruption Duration (s)** \\ \hline
70 & 4 & 450 & 2 & 616 & 6 \\
96 & 2 & 465 & 5 & 650 & 6 \\
156 & 8 & 471 & 3 & 662 & 8 \\
168 & 14 & 474 & 4 & 686 & 12 \\
183 & 25 & 482 & 10 & 717 & 6 \\
241 & 25 & 511 & 7 & 730 & 5 \\
320 & 6 & 543 & 5 & 775 & 7 \\
366 & 2 & 597 & 11 & & \\
413 & 9 & 608 & 7 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: GNSS interruption time statistics for Section 1.  
Figure 7: Test vehicle driving route.  
Regarding the long straight section with frequent interruptions, considering the eastward error curve in Figure 10 and the northward error curve in Figure 11 (580-1000 s), the vehicle's motion pattern on the straight road is relatively simple, as the vehicle moves in a straight line. This simplicity allows for more accurate and reliable position predictions based on historical data. Utilizing the LSTM and CNN-GRU neural network models enhances the precision of position predictions, thus reducing the possibility of positioning errors.  
From the position error statistics in Table 3, it can be seen that CNN-GRU-FGO, compared to FGO and LSTM-FGO, has reduced the root mean square error of position by 89% and 56%, respectively. In environments with frequent GNSS interruptions, both LSTM-FGO and CNN-GRU-FGO significantly enhance position accuracy compared to the traditional FGO algorithm. CNN-GRU is capable of capturing long-term dependencies and dynamic changes in time series, better adapting to these complex dynamic environments. As a result, in settings with frequent GNSS interruptions, CNN-GRU-FGO is able to provide more accurate and stable vehicle positioning results.  
Figure 11: Section 1 north error.  
Figure 10: Section 1 east error.