# On the Use of a Feedback Tracking Architecture for Satellite Navigation Spoofing Detection
## 6 Results Using the Spoofing Detection Algorithm
### Numerical Results for Detection and Latency  
In this section, we provide numerical results for the ECADLL algorithm. These results were obtained by processing four datasets with spoofing signals from the TEXBAT and three different clean datasets, one from the clean scenario in the TEXBAT and the other two obtained by means of a moving vehicle in downtown Turin, Italy. This is by no means a comprehensive set of datasets with all possible configurations of spoofing signals, but we believe that they provide a very insightful initial result for demonstrating the capabilities of ECADLL, the detection algorithm and its future potential. First, we present a confusion matrix including all of the satellites for the different scenarios that where tested and how they where classified by the algorithm. A confusion matrix is a useful tool to describe the performance of a classification algorithm, such as the one proposed in this paper. In Table 2, we can observe the confusion matrix and how the different satellites where classified. For the spoofed scenarios, we observed six satellites for each of the four datasets, obtaining a possible of 24 spoofed satellites; the ECADLL algorithm classified 22 of them correctly and missed the detection of two, not detecting correctly one satellite in each of scenarios ds6 and ds7. We believe that these misdetection where caused by the low \(C/N_{0}\) that these satellites had for this configuration of the re-transmission, and the algorithm was not able to distinguish clearly the external signal that was present in the correlation domain. Figure 17: Comparison of the impairment detection using GPS Satellite Number 15 for the dynamic dataset, TEXBAT ds6. In red, the decision made by ECADLL is shown and in blue, the one using SQMT. On the other hand, no false alarms where present during the clean scenarios, and none of the satellites were wrongly classified. Following the confusion matrix, which summarizes the detection capabilities of the technique, we will now focus on the improvements that the introduction of RM brought to ECADLL. Two main features where affected by RM, and they will be defined as Computational Time (CT) and Detection Latency (DL). CT will be defined as the time that the algorithm takes to process a single satellite of one scenario of the TEXBAT datasets, and DL is the time difference between the beginning of the push-off phase of the spoofing signal and the first detection of the algorithm, either as an impairment or spoofer. Of course, these values will change between configurations of the receiver and of the spoofing, computer implementation and characteristics of the datasets. Therefore, they should not be considered as standalone values, but we can obtain useful information if we compare the same dataset while ECADLL is using RM or not. In Table 3, we can observe the numerical improvement for each of the different datasets. In the Computational Time Improvement (CTI), defined as \(CTI=(CT_{base}-CT_{RM})/CT_{base}\), we can see how the biggest improvement was obtained when the clean dynamic scenario was used; this means when no spoofer was present. This result is intuitive because when no spoofer is present, the use of RM for the activation of the secondary units will prevent ECADLL from having the two units active at all times. The presence of the Unit1 by itself represents a high burden on the computational load of the receiver, given that it can be comparable to having twice as many channels in tracking and, thus, twice as many correlators. The scenarios with the presence of spoofing signals do not get that much improvement, because while the spoofer signal is present, both configurations are running two units for each channel. This means that the improvement will be weighted by the amount of time the spoofing signals are not present in the dataset, which for ds3 and ds6 is about 1/4 and for ds7 and ds4 is around 1/3 of the total time. Eventually, we focus our attention on the Detection Latency Improvement (DLI), defined as \(DLI=(DL_{RM}-DL_{base})/DL_{base}\).