## 5 Results and Discussion
### Detection of Interference  
Figure 6 and the related sub-figures show the detection results using the feature-based approach for the metric of accuracy, Figure 7 and sub-figures show the precision, and Figure 8 and sub-figures show the recall for different levels of attenuation. In particular, Figure 6a, Figure 6b, and Figure 6c show, respectively, the detection accuracy for low, medium, and high levels of interference for the three types of interference and different levels of bit-depth (called \(B_{D}\) in the rest of this section) as shown in the legend. In a similar way, Figure 7a-c show the precision values and Figure 8a-c show the recall values. Regarding \(B_{D}\), the results are generally consistent across the metrics and the types of interference: a higher value of \(B_{D}\) (e.g., 16 bits in comparison to 2 bits) provides a higher value of the metric (e.g., accuracy). The differences are more significant for low levels of interference, where the detection algorithm has more difficulty in detecting the interference. This is to be expected because a high value of \(B_{D}\) provides richer and more discriminating information to the detection algorithm, which supports a better detection of the interference. However, the figures seem to indicate that \(B_{D}=16\) bits are not needed to achieve the optimal results. Values of 8 bits also provide a detection performance comparable to 16 bits. These results can be used to reduce the amount of data needed to perform the detection of interference.  
The higher the level of interference, the higher the value of the performance (e.g., higher accuracy). This is also to be expected because the higher the level is the more significant the impact on the GNSS signal, which translates to a higher variation in the \(C/N_{0}\) time series.  
The results for the different types of interferences are somewhat varied and they depend on the level of interference. This may also be related to the different interference power levels defined in Table 2. Narrow interference can be detected more easily than Wideand Gaussian interference for low levels of interference, but the Gaussian interference is detected with higher accuracy with the higher power level of interference.  
Similar results are achieved using the CNN algorithm. To conserve space, only the accuracy results are shown because the precision and recall graphs present similar results. The comparison of Figure 9 with Figure 6a-c shows that the CNN is able to obtain a superior detection performance at the cost of a higher computing complexity. In particular, for higher values of \(B_{D}\), the CNN algorithm can obtain perfect detection (i.e., 100%) or near perfection. For the value of \(B_{D}=1\), even the CNN algorithm has a significant number of misclassification errors because the lower bit-depth degrades significantly discriminating features, which can be used by the CNN to detect the interference. As expected (and coherently with the feature-based approach), a low level of interference causes a lower detection accuracy, especially for the Wide and Narrow types of interference, while the CNN reaches almost perfect detection for values of \(B_{D}\) higher or equal than 4.  
Figure 6: Comparison of the detection with the accuracy metric using the feature-based approach. (**a**) low interference level, (**b**) medium interference level, and (**c**) high interference level.  
Figure 7: Comparison of the detection with the precision metric using the feature-based approach. (**a**) low interference level, (**b**) medium interference level, and (**c**) high interference level.