# An Accurate GNSS Spoofing Detection Method Based on Multiscale Eye Diagrams
## 4 Detection Coverage Simulation Experiment  
Detection coverage is a key metric for evaluating the defensive efficacy of methods against various deception techniques. This section offers a detailed introduction to the commonly used CN0 and SQM (Delta, Ratio, Slope, ELP, and the combined metrics Delta+Slope and Ratio+ELP) detection methods, comparing them experimentally with the EDDM-MSC-MMP method proposed in this study. It presents simulation results of detection coverage for different methods when dealing with various spoofed signals, which vary in power, code phase offsets, and carrier offsets. The L1 signal of the GPS 8 satellite was employed to conduct a spoofing simulation. A total of 4,410 grid experiments were conducted, each lasting 10 seconds, with spoofing occurring for a period of 5 to 10 seconds. The receiver employed was a GNSS software-defined radio. The signal simulation parameters were set as follows: the range of the relative power advantage of the spoofing signal was 1 dB to 10 dB, with a step size of 1 dB; the range of the code phase offset \(\Delta\tau_{s}\) was 0 to 1 chip, with a step size of 0.05 chips; and the carrier phase offset \(\Delta\theta_{s}\) ranged from 0 to \(2\pi\). The step size for the spoofing signal was \(0.1\pi\), resulting in a noise level comparable to that of the real signal. The false alarm rate was \(P_{f}\leq 0.01\). All other parameters of the spoofing signal were identical to those of the real signal. To ensure the timely detection of spoofing, the predetection integration time was \(T_{d}=0.005s\). One spoofing decision was made per millisecond, and the detection rate was calculated after each experiment. The rate of detection is defined as follows:  
\[\text{P}_{\text{d}}=\frac{\text{Time}\{\text{Measurements exceed the threshold}\}}{\text{Time}\{\text{Presence of spoofing signal}\}} \tag{19}\]This is the ratio of the time during which the metric measurement exceeds the threshold (the time during which spoofing is detected) to the total time during which the spoofing signal is present. The detection results are shown in Figure 4. The first column shows a three-dimensional representation of the detection results, and the second column shows the corresponding slices. The X-axis denotes the relative code phase offset of the spoofing signal, the Y-axis indicates the relative carrier phase offset of the spoofing signal, and the Z-axis denotes the relative power advantage of the spoofing signal. The color of the grid indicates the detection rate. As illustrated in the figure, the two SQM metrics, Delta and Slope, exhibit a notable degree of correlation in their detection rates. Their detection rates are low because both metrics perform spoofing detection on the basis of unilateral information about the relevant peak, which results in a relatively low level of accuracy. In contrast, the ELP and Ratio metrics adopt a complementary approach that utilizes bilateral information from the relevant peaks, thereby improving the detection performance to a certain extent. While the SQM metric combination exhibits better detection performance than the use of a single metric, this improvement is constrained by the inherent limitations of the original single metric. In particular, the combination of Delta and Slope does not yield a significant improvement in the detection rate, and the combination of Ratio and ELP also results in only very limited performance gains. The CN0-based detection method has poor detection results because spoofing signals have the same noise characteristics as the real signal, and, even though spoofing signals have a relative power advantage, the \(C/N_{0}\) change in the receiver is not obvious. In contrast, the overall detection rate of the EDDM-MSC-MMP is significantly greater. This is because the EDDM-MSC-MMP employs a strategy of real-time monitoring of amplitude changes in the correlation value following the receiver integration-cancelation process.