# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 6 Performance Evaluation with TEXBAT
### Scenario 6 of TEXBAT  
Detection results of SQM and RAIM in Scenario 6 are shown in Figure 12. Different from Scenario 2, the ratio metrics and SSEs occasionally exceed the threshold. The abnormalities before 110 s are probably caused by the dynamics of the receiver. There are abnormalities between 150 and 250 s since the spoofing signals try to mislead the code phases and cause disruptions in the tracking loops of the receiver. After drag-off, both the metrics are normal again, but the receiver is completely spoofed and can only output falsified PVT results. Figure 12 demonstrates that the SQM and RAIM techniques cannot deal with the spoofing attack in Scenario 6.  
Figure 13a shows the test statistics in Scenario 6. They are smaller than the threshold in the first 110 s when no spoofing signals are present. From 110 s to 150 s, spoofing signals are added, but their code phases are aligned with those of the authentic ones, and their powers are only slightly higher than those of the authentic ones. Consequently, the spoofing and authentic signals will be canceled simultaneously, and the signal component in the residual signal \(\mathbf{y}\) is very weak. Hence, the test  
Figure 11: (**a**) \(r_{2}\) in Scenario 2; results larger than four indicate that consistent signals are detected in the residual signal and the spoofing attack is validated; (**b**) Biases of the user clock offsets from the clock offset in Epoch 0: \(\delta t_{u}=t_{u}(t)-t_{u}(0)\). The green trace shows the receiver’s unspoofed response; the blue trace shows the receiver’s spoofed response; and the red dots show the recovered results.  
statistics do not increase obviously. After 150 s, the test statistics increase because the code phase differences between the authentic and spoofing signals increase. Similar to Figure 10a, the statistics stop increasing after the peaks of the authentic and spoofing signals are completely separated.  
Figure 13b shows the detection results of Scenario 6. It can be seen that when only one signal is processed, the detection probability in a dynamic receiver is much lower than that in a static receiver due to the motion of the receiver and the changing environment. However, the detection performance is still very good when all of the signals are processed together, since it takes advantage of the diversity gain of multiple signals.  
Figure 12: (**a**) Ratio metric of SQM in Scenario 2; (**b**) SSE of RAIM in Scenario 2; the red dashed line denotes the threshold.  
Figure 13: (**a**) Test statistics and threshold in Scenario 6; (**b**) Detection results in Scenario 6; results larger than 0.5 mean that there are evil signals. The red circles correspond to the results when seven signals are processed together, and the blue points correspond to the results when one signal is processed.  
Figure 14a shows \(r_{2}\) in Scenario 6. It is larger than four after 180 s, indicating successful spoofing validations.  
Figure 14b shows the biases of user position results in Scenario 6. Since the spoofing signals in this scenario only falsify the position results in the z-coordinate, only the biases in this dimension are shown. Figure 14b shows that the recovered position results are also very close to the receiver's unspoofed response.