## 4 Experimental Results
### Method Validation and Performance Analysis  
Figure 13 shows the estimation of azimuth obtained from CNR and CPSD estimation. It can be seen that there is a high similarity between the estimation of spoofing from the same direction. Due to differences in MLE algorithms, the initial phase of the two estimated values is different. Therefore, there is a difference between the two estimated values in the figure. When calculating the detection variables, the initial phase will be eliminated by difference. \begin{table}
\begin{tabular}{l l} \hline \hline Parameter setting of the receiver & \\ \hline Rotating radius & 0.15 m \\ Tilt angle & 30\({}^{\circ}\) \\ Rotational angular velocity & 18 \({}^{\circ}\)/s \\ Sampling frequency & 10 Hz \\ Position coordinates & (0,0,0) \\ Number of samples in MLE & 400 \\ \hline Parameter setting of spoofers & \\ \hline Spoofer A & Spoofer B \\ Position coordinates & (0,2,0.5) \\ PRN of spoofing & 10,12,15 \\ \hline Parameter setting of spoofing & \\ \hline Start time of experiment & 2023/09/11 06:12:05 (UTC) \\ Experiment duration & 490 s \\ Start time of spoofing & 80 th second \\ Type of spoofing & Time spoofing \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameter settings of the experiment. Figure 12: Navigation solution in the experiment. After processing the data in the spoofing experiment, the spoofing-detection method proposed in this article was verified and its performance was analyzed. To analyze the impact of the components of the detection variable \(SSE\) on detection performance, the detection performance of \(SSE_{1}\), \(SSE_{2}\), \(SSE_{3}\), and \(SSE\) was evaluated. \(SSE_{1}\) is determined by the azimuth angle estimated from the CNR, \(SSE_{2}\) is determined by the azimuth angle estimated from the CPSD, and \(SSE_{3}\) is determined by the elevation angle estimated from the CPSD. Figure 14 shows the variation of detection variables under different satellite combinations. To facilitate observation, the detection variables of each pair of combinations are distinguished by color and pattern. The satellite PRNs represented by different colors and patterns are described in the caption. The black dashed line in the figure indicates the optimal detection threshold calculated based on the Neyman-Pearson criterion. The value of the detection variable that is greater than the threshold indicates that the pair of signals contains a spoofing signal. It should be noted that 40 s of data are required for parameter estimation using MLE, so the length of the detection variable is 450 s. By selecting different thresholds and obtaining the detection probability and false alarm probability under different thresholds, the ROC shown in Figure 15 is obtained on this basis. The optimal detection probability and optimal false alarm probability of \(SSE_{1}\), \(SSE_{2}\), \(SSE_{3}\), and \(SSE\) in the experimental data were calculated according to the Neyman-Pearson criterion, as shown in Table 3.  
By observing Figure 14, it is found that: (1) \(SSE_{1}\) has good detection performance in the initial stage of spoofing but has a high probability of missed detection during the continuous spoofing process; (2) \(SSE_{2}\) and \(SSE_{3}\) have poor detection performance in the initial stage of spoofing, but have good detection performance during the continuous spoofing process; (3) \(SSE\) has good detection performance in both the initial spoofing stage and the continuous spoofing stage. By observing Figure 15 and Table 3, it is found that the detection performances of \(SSE_{1}\) and \(SSE_{2}\) are close to each other and higher than that of \(SSE_{3}\), and the performance of the spoofing detection using the elevation angle is slightly lower than that using the azimuth angle. The detection performance of \(SSE\) is significantly better than that of \(SSE_{1}\), \(SSE_{2}\), and \(SSE_{3}\), indicating that using both CNR and CPSD for spoofing detection has better detection performance.