## 4 Results Discussion
### SDR Module Results  
The first and primary aspect to analyze in the behavior of the SDR module's algorithm was its ability to detect signals from intrusive UAVs. For this purpose, tests were conducted in different environments where a UAV was flown at various distances to determine if the system can indicate the presence of a UAV. Figure 11 shows the results of all the tests conducted, illustrating the detection success rate as a function of the distance to the UAV. The success rate is simply the percentage of times the UAV was detected by the algorithm out of the total number of times the UAV was flown at that distance. The results show that the UAV detection system achieved a performance of practically 100% at distances of 10 and 20 m, indicating high accuracy and reliability at close ranges. However, as the distance increased, the detection efficiency decreased. This trend can be explained by the signal strength received by the SDR; the further away the UAV is, the lower the received power, making it more challenging for the algorithm to identify anomalies in the received signals. Nonetheless, the results for these longer distances are still favorable, with successful detection rates of 89% and 83% at distances of 30 and 40 m, respectively, dropping to the lowest value of 80% at a distance of 50 m.  
It is important to note that the effectiveness of the algorithm is significantly influenced by the dataset used to train the autoencoders. Generally, the larger the dataset, the better the detection algorithm will perform, as it will have been trained with a greater number of samples. This is particularly crucial in highly saturated bands like those used by Wi-Fi, where there are many interfering signals. A more extensive dataset would include more samples of all these non-UAV signals. Ideally, when installing the system, the algorithm should collect data from the spectrum of each band for an entire day. This would provide samples of the radio spectrum's state in each band when no UAV is present at different times of the day, which is important as the signals present may vary throughout the day. For these tests, the training was  
Figure 11: Detection test results using the SDR module as a function of distance from the UAV. conducted with data collected over periods of less than two hours, so, with more data, detection could be more effective. The second aspect analyzed was the average time taken to detect the UAV as a function of distance. As mentioned earlier, greater distances can make detection more challenging due to the weaker signal strength received, which also results in increased detection time. Figure 12 illustrates this situation, clearly showing that, as the distance increases, the time required for the algorithm to identify the presence of the UAV also increases. At a distance of 10 m, the detection time was minimal because, as previously explained, determining the presence of a UAV requires detecting an anomaly in the same part of the frequency band in two consecutive readings of the same band. Therefore, the time needed to perform two readings of the same band is approximately what was observed for the 10 m distance. As the distance increases, the detection time also increases because the UAV is not always detected in two consecutive readings. If the UAV is not detected in one reading, at least two more readings are needed to make the detection, thereby increasing the time required. The greater the distance, the weaker the signal received, making it more likely that detecting the UAV will take longer. This approach is designed to minimize the number of false positives that the system might produce. By requiring two consecutive readings with anomalies in the same part of the band, the system nearly eliminates false positives, though it makes detection more challenging. During testing, the algorithm's resistance to false positives was also analyzed by running the system to monitor the frequency bands without flying any UAVs, to check if the UAV alarm would trigger when there was none.