# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Feature Visualization  
To intuitively understand the recognition features learned by LTS-SEI, Figure 8 shows the 2D scatter maps of features extracted by different methods for A1->A2, A1->A3, B1->B2, and B1->B3. The first column shows the 2D features of the target domain samples output by the model directly trained using the source domain samples. For A1->A2 and A1->A3, it is evident that some features of Specific Emitters G, H, and J are confused. This indicates that even if the time span between signals is small, the trained model is likely to reduce its predictive ability for new data. For B1->B2 and B1->B3, which have longer time spans, the trained model completely confuses the two specific emitters. The second column displays the 2D features of target domain samples output using the existing domain adaptation methods. The confusion between specific emitters G, H, and J in A1->A2 and A1->A3 was alleviated through domain adaptation. However, the two specific emitters in B1->B2 are still unrecognized, and some samples in B1->B3 are confused. The third column presents the 2D features of the target domain samples output by the proposed LTS-SEI method. Clearly, LTS-SEI effectively separates the features of different specific emitter samples. Thanks to the mutual cooperation between its different components, LTS-SEI can extract RFFs with  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Recognition Accuracy**} \\ \cline{2-7}  & **A1->A2** & **A1->A3** & **B1->B6** & **B1->B7** & **B1->B8** & **B1->B9** \\ \hline I/Q & 0.8958 & 0.8592 & 0.9450 & 0.4842 & 0.5758 & 0.6817 \\ Center & 0.9550 & 0.9150 & 0.9533 & 0.4800 & 0.5750 & 0.6808 \\ MMD & 0.9542 & 0.9342 & 0.9467 & 0.5008 & 0.5742 & 0.6883 \\ DAN & 0.9758 & 0.9258 & 0.9517 & 0.4875 & 0.5850 & 0.6617 \\ JDA & 0.9517 & 0.9425 & 0.9483 & 0.4917 & 0.5850 & 0.6483 \\ CMD & 0.9283 & 0.8925 & 0.9575 & 0.4625 & 0.5633 & 0.6500 \\ HOMM & 0.9875 & 0.9575 & 0.9617 & 0.4850 & 0.6242 & 0.7150 \\ LTS-SEI & **0.9995** & **0.9971** & **0.9892** & **0.9567** & **0.9458** & **0.9850** \\ Upper Limit & 0.9996 & 0.9992 & 0.9962 & 0.9983 & 0.9983 & 0.9975 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance comparison between various methods (deep layer).  
interclass separation, intraclass compactness, and domain invariance. Undoubtedly, these RFFs are crucial in identifying the long time span-specific emitter signals.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Average Recognition Accuracy**} \\ \cline{2-9}  & **A1-\textgreater{}A2** & **A1-\textgreater{}A3** & **B1-\textgreater{}B10** & **B1-\textgreater{}B11** & **B1-\textgreater{}B12** & **B1-\textgreater{}B13** & **B1-\textgreater{}B14** \\ \hline I/Q & 0.8958 & 0.8592 & 0.5083 & 0.4917 & 0.7308 & 0.5367 & 0.5150 \\ MMD & 0.9358 & 0.8467 & 0.5108 & 0.5200 & 0.7567 & 0.5558 & 0.5167 \\ DAN & 0.9117 & 0.8742 & 0.5550 & 0.4958 & 0.7892 & 0.5658 & 0.5233 \\ JDA & 0.9133 & 0.8425 & 0.5393 & 0.5092 & 0.7850 & 0.5283 & 0.5067 \\ CMD & 0.9058 & 0.8842 & 0.5358 & 0.5025 & 0.7208 & 0.5433 & 0.4858 \\ HOMM & 0.9533 & 0.9242 & 0.5383 & 0.5208 & 0.7417 & 0.5500 & 0.5025 \\ LTS-SEI & **0.9995** & **0.9971** & **0.9733** & **0.9392** & **0.9942** & **0.9533** & **0.9633** \\ Upper Limit & 0.9996 & 0.9992 & 0.9992 & 0.9979 & 0.9967 & 0.9933 & 0.9971 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison between various methods (shallow layer and deep layer).  
Figure 8: \(Cont.\)