# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Performance Comparison with Domain Adaptation Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Average Recognition Accuracy**} \\ \cline{2-6}  & **A1->A2** & **A1->A3** & **B1->B2** & **B1->B3** & **B1->B4** & **B1->B5** \\ \hline I/Q & 0.8958 & 0.8592 & 0.5200 & 0.8650 & 0.4792 & 0.4692 \\ MMD & 0.9108 & 0.8933 & 0.5400 & 0.8458 & 0.4692 & 0.5008 \\ DAN & 0.9367 & 0.8883 & 0.5456 & 0.8708 & 0.4667 & 0.4883 \\ JDA & 0.9425 & 0.8867 & 0.52 NAS & 0.8542 & 0.4975 & 0.4933 \\ CMD & 0.9833 & 0.9417 & 0.5308 & 0.8467 & 0.4625 & 0.4700 \\ HOMM & 0.9842 & 0.9492 & 0.5367 & 0.8433 & 0.4842 & 0.4967 \\ UDAB & 0.9975 & 0.9925 & 0.5550 & 0.9425 & 0.5208 & 0.5283 \\ LTS-SEI & **0.9995** & **0.9971** & **0.9867** & **0.9942** & **0.9650** & **0.9642** \\ Upper Limit & 0.9996 & 0.9992 & 0.9987 & 0.9983 & 0.9992 & 0.9929 \\ \hline \hline \end{tabular} Note. “A1->A2” indicates that the model trained using Source Domain Data A1 predicts the labels of Target Domain Data A2. There is a time interval between A1 and A2.  
\end{table} 
Table 4: Performance comparison between various methods (shallow layer). features. This may be because deep features play a decisive role in predicting the labels of specific emitters. However, it is difficult for the existing methods to learn domain-invariant deep features of the long time span-specific emitter signals in Dataset B. Although there may be considerable differences in data distribution between B7, B8, B9 and B1, the proposed LTS-SEI method still enjoys a considerable improvement in identifying two specific emitters. _Shallow layer and deep layer_: Additionally, we trained the model to simultaneously align shallow RFFs and deep RFFs simultaneously. Unfortunately, this idea did not achieve better results. For A2 and A3, the existing methods suffered overfitting when the model simultaneously learned domain-invariant shallow features and deep features (see Table 6). Compared with the two feature learning methods previously mentioned, the average recognition accuracy of HOMM on A2 and A3 decreased by 3.09% and 3.42%, 2.5% and 3.33%, respectively. The collection time interval between B14 and B1 is close to one and a half years. The recognition accuracy of the original I/Q signal and existing methods on B14 is near 50%, which means that the model lost its predictive ability. Therefore, even if a high-gain antenna can receive high-quality signals, it is likely to cause considerable deviation in data distribution between the training set and testing set when the time span is long. This may be the result of the simultaneous effect of external environment and internal RFF changes. The same concept applies to other datasets. However, the proposed LTS-SEI method can still extract stable or slowly changing shallow features and deep features, which demonstrates the effectiveness of LTS-SEI in addressing the long time span SEI problem. We suppose that, the satisfactory recognition or prediction performance of LTS-SEI is inseparable from the decent signal quality of Datasets A and B.