# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 5 Experimental Analysis under Forwarding Spoofing Attack
### Urban  
For the urban scene shown in Figure 5b, the simulation is the urban scene where the mobile terminal is spoofed and pulled to another road. So, we choose the segment from 380 s to 430 s of the data set and the terminal is exposed to a GNSS spoofing with 133 m spoofing distance, and the mobile terminal is simulated to be deflected to the adjacent road. Figure 12 shows the absolute position errors of GNSS, VINS-Fusion and PTC-SLM after GNSS forwarding spoofing attacks. Figure 13 shows the movement trajectory of the three methods of GNSS, VINS-Fusion, and PTC-SLM. Their maximum value (Max), average value (Mean), median value (Median), minimum value (Min), rmse and std of the absolute position errors of the three methods are shown in Table 6.  
Figure 11: Trajectories of the three methods in rural scene. (**a**) GNSS, (**b**) VINS-Fusion (**c**) PTC-SLM.  
Figure 10: The absolute position error of the three methods in rural scene.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Max/m** & **Mean/m** & **Median/m** & **Min/m** & **Rmse/m** & **Std/m** \\ \hline GNSS & 21.088 & 3.138 & 1.252 & 0.508 & 6.459 & 5.646 \\ VINS-Fusion & 27.421 & 3.310 & 1.304 & 0.398 & 6.644 & 5.761 \\ PTC-SLM & 2.928 & 1.285 & 1.217 & 0.383 & 1.342 & 0.390 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Error parameters of the three methods in rural scene.  
It can be seen that when the GNSS method suffers GNSS forwarding spoofing, the GNSS localization results are directly affected by the GNSS forwarding spoofing and are incorrectly located on an adjacent road. The absolute position error of GNSS method is shown in the blue curve in Figure 12. The error on trajectory is shown in Figure 13a and the rmse of GNSS method increased to 33.918 m.  
With the injection of GNSS forwarding spoofing, the localization results of the VINS-Fusion method will deviate from the normal track. When the GNSS attack is removed, the localization results after the VINS-Fusion method will oscillate and gradually return to the right track. The absolute position error is shown in the brown curve in Figure 12, and the error on the trajectory is shown in Figure 13b. The rmse of VINS-Fusion is 33.361 m.  
The absolute position error of the PTC-SLM method is shown in the green curve in Figure 12. It can be seen that the PTC-SLM method is positioned smoothly, and the error on the trajectory is shown in Figure 13c. It can be seen that the localization result is still basically coincident with the reference, and the rmse is 1.579 m, with an improvement of 95.345% compared with the GNSS method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Max/m** & **Mean/m** & **Median/m** & **Min/m** & **Rmse/m** & **Std/m** \\ \hline GNSS & 134.439 & 9.610 & 1.153 & 0.189 & 33.918 & 32.528 \\ VINS-Fusion & 153.538 & 10.060 & 1.224 & 0.096 & 33.361 & 31.808 \\ PTC-SLM & 23.868 & 1.261 & 1.182 & 0.067 & 1.579 & 0.950 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Error parameters of the three methods in urban scene.  
Figure 12: The absolute position error of the three methods in urban scene.  
Figure 13: Trajectories of the three methods in urban scene. (**a**), GNSS (**b**), VINS-Fusion, (**c**) PTC-SLM.