# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 6 Experimental Analysis under Inducing Spoofing Attack
### Urban  
For the urban scene shown in Figure 5b, the segment from 450 s to 470 s of the data set is exposed to a GNSS inducing spoofing (a 0.5 m distance is superimposed at each GNSS  
\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 & 161.118 & 6.700 & 1.234 & 0.508 & 24.497 & 23.563 \\ VINS-Fusion & 145.038 & 7.083 & 2.632 & 0.042 & 21.191 & 19.972 \\ PTC-SLM-Forward & 53.149 & 3.130 & 1.234 & 0.281 & 8.508 & 7.911 \\ PTC-SLM & 16.120 & 1.780 & 1.240 & 0.401 & 2.870 & 2.251 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Error parameters of the four methods in rural scene.  
Figure 16: The absolute position error of the four methods in urban scene.  
Figure 17: Trajectories of the four methods in urban scene. (**a**) GNSS, (**b**) VINS-Fusion, (**c**) PTC-SLM-Forward, (**d**) PTC-SLM.  
localization moment). Figure 16 shows the absolute position errors of GNSS, VINS-Fusion, PTC-SLM-Forward, and PTC-SLM after GNSS inducing spoofing attacks. Figure 17 shows the movement trajectory of the four methods of GNSS, VINS-Fusion, PTC-SLM-Forward, 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 four methods are shown in Table 8.  
It can be seen that the absolute position error of GNSS method grows gradually as the GNSS inducing spoofing attacks occur as shown in the blue curve in Figure 16. The error on trajectory is shown in Figure 17. The rmse of GNSS increased to 11.802 m. The localization results of VINS-Fusion gradually deviate from the normal track and the absolute position error is shown in the brown curve in Figure 16, and the error on the trajectory is shown in Figure 17. The rmse of VINS-Fusion is 10.441 m.  
The PTC-SLM-Forward method did not identify GNSS inducing spoofing attacks in time. The GNSS inducing spoofing is input into the smoothing localization process, so that the localization result obtained by the PTC-SLM-Forward method is continuously shift to the GNSS inducing localization direction. The absolute position error is shown in the green curve in Figure 16, and the error on the trajectory is shown in Figure 17. The rmse of PTC-SLM-Forward is 10.399 m.  
The PTC-SLM method can detect GNSS inducing spoofing attacks in time to suppress the impact of GNSS inducing spoofing. The absolute position error is shown in the red curve in Figure 16, and the error on the trajectory is shown in Figure 17. When the GNSS spoofing is identified and the GNSS frame is discarded, the PTC-SLM method maintains the final localization output with the result calculated by the visual SLAM system and generates a visual cumulative error. It can be seen that the rmse of PTC-SLM is 2.477 m, with an improvement of 79.012% compared with the GNSS method.  
Through the analysis results of Sections 5 and 6, it can be seen that the PTC-SLM method can well identify GNSS forwarding spoofing and GNSS progressive spoofing can obtain better smoothing localization results compared with other methods. Besides, the localization performance of PTC-SLM under forward spoofing attack is better than that under progressive spoofing attack.