# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 7 Conclusions  
Given the vulnerability and insufficient awareness of the UAV data link to the EMI threat, this paper proposes a multi-task CNN with multi-input (MIMT-CNN) to realize the EMI signal identification and risk assessment of the UAV data link. Based on the multi-task learning and multi-information fusion, the network extracts EMI features by inputting visualized performance parameters, STFT spectrogram and normalized density constellation, and then fuses these features by using the parallel structures optimized by the Bayesian algorithm. By extending the traditional serial structure, the network can extract more complex EMI signal features and make full use of the correlation information among the input features through multi-task outputs. Compared with learning each task separately, it can reduce the time cost, calculation cost, and storage cost of the network.  
By changing the categories of the input images of the MIMT-CNN for training and testing, it is found that the visualized performance parameters are the basis of model validity,  
Figure 10: Effect comparison of typical networks.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Reference** & **Accuracy** & **RMSE** & **Algorithm** & **Application Scenario** \\ \hline \multicolumn{3}{l}{Interference identification and threat assessment (this work)-Multi-task} \\ \hline Proposed Model & 95.4\% & 0.49 & CNN \\ \hline Interference identification- Single-task & & & \\ \hline
[34] & 86.97\% & - & CNN & Cognitive radio equipment \\
[35] & 89\% & - & U-Net & Radar receiver \\
[36] & 92.7\% & - & FCN & Engine digital controllers \\
[37] & 96.53\% & - & ResNet & High voltage power plants \\ \hline \multicolumn{3}{l}{Threat assessment- Single-task} \\ \hline
[38] & - & 0.975 & DNN & Financial loss \\
[39] & - & 0.518 & CNN-LSTM & Water quality risk \\
[10] & - & 0.3882 & CNN & Data link under EMI \\
[40] & - & 0.231 & DNN & Flood risk \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of model performance.  
while the STFT spectrogram or constellation diagram can improve model accuracy. Among them, the network with the best classification and regression performance can be obtained by inputting the visualized performance parameters, STFT spectrogram, and normalized density constellation, but the time cost will increase with the network complexity.  
The cognition of EMI types and threat levels contributes to the self-awareness of the data link. Furthermore, it can be combined with interference suppression technology to achieve a fast and accurate confrontation strategy on the UAV platform, so that the UAV will obtain the ability of self-healing.  
Conceptualization, T.X. and Y.C.; methodology, T.X.; software, T.X.; validation, Y.W., D.Z. and Y.C.; formal analysis, T.X. and M.Z.; investigation, D.Z.; resources, Y.W. and Y.C.; data curation, T.X. and M.Z.; writing--original draft preparation, T.X.; writing--review and editing, Y.W.; visualization, D.Z. and M.Z.; supervision, Y.C.; project administration, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data are not publicly available due to the data confidentiality requirements of the unit. Anyone who needs the code can contact the author, and the code can be given after certain evaluation procedures.  
The authors declare no conflict of interest.