# Antiference: New Concept for Evolutive Mitigation of RFI to GNSS +
## 6 Test and Validation Results  
We have seen good detection capabilities for both jamming and spoofing. The classification of individual signal chunks has yielded good results, and has proven to provide useful information for the mitigation stage. Table 1 summarizes some of the results.  
Having knowledge of the specifics of a jamming attack can lead to better mitigation, as was demonstrated in the validation phases. The effectiveness of the mitigation method has not been fully demonstrated yet in the user domain; although Antiference does filter jamming signals effectively and shows significantly improved C/N0 values for medium- to high-power jammers, the resulting improvement in the PVT-domain has not been convincingly demonstrated. The main reason for the shortcomings comes from the limitations in the digital domain, as the mitigation/filtering was performed only on the digitized signals, where the degradations of the RFI cannot be fully recovered. In a future evolution, the overall mitigation performance in the user domain can be improved by applying the mitigation directly at the receiver level.  
Antiference has been used in combination with two COTS receivers and has been compared to Rec02's internal RFI suppression. As the Rec01's internal RFI mitigation cannot be turned off, Antiference can only be used in combination with it and not compared with it in isolation. The relative performances differ for the different interference types. Antiference performance is typically comparable to that of Rec02, while, in some cases, it is clearly somewhat better or somewhat worse than Rec02. Further optimization is expected to further improve the Antiference performance, but, for example, for FM interference, it already performs remarkably well, as shown in Figure 3 It is, however, hard to distinguish FM and AM interferers; however, due to the identical mitigation strategy, this does not impact performance negatively (see Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Datasets** & **AM** & **FM** & **SCW** & **CW** & **AGW** \\ \hline False Alarm Rate & 0.001 & 0.002 & 0.002 & 0.0 & 0.002 \\ \hline Detection Accuracy & 1.0 & 1.0 & 0.9 & 1.0 & 0.952 \\ \hline \end{tabular}
\end{table}
Table 1: Detection accuracy of the Antiference system.  
Figure 3: The improvement in observed C/N0 in the presence of FM interference for nine of our recordings (that are depicted head to tail). Antiference shows a better interference suppression than the Rec02 COTS receiver.  
In addition, we have looked at the outcomes of the Antiference Logs that provide data on the internal workings of the module, particularly in relation to the false-detection rates. The Antiference algorithms turned out to be highly sensitive, as they effectively detect low-power CW and SCW (chirp) interferences close to 100% of the time. This is not necessarily desirable, as the detection of such low-impact interferences is not needed and comes at the expense of higher-than-needed false-detection rates and lower quality signal due to the removal of some of the signal because of filtering. A simple remedy is the use of a simple majority voting scheme over multiple subsequent RFI classifications. This has been shown to reduce the false detection rates effectively, but at the expense of an increased time-to-detection.