# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Performance Comparison with Domain Adaptation Methods  
In this study, the long time span SEI problem is considered a domain adaptation problem. Therefore, LTS-SEI is compared with the existing domain adaptation meth \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Meaning** & **Model A** & **Model B** \\ \hline \(\alpha\) & Center Loss weight & 0.1 & 0.01 \\ \(\beta\) & HOMOM3 Loss weight & 0.1 & 0.01 \\ \(l\) & Initial learning rate & 0.0007 & 0.001 \\ \(\mu\) & Learning rate & 0.01 & Equation (25) \\ \(\lambda\) & Reversal Scalar & 0.01 & Equation (26) \\ \(E\) & Epoch & 100 & 100/300 \\ \(B\) & Batch Size & 64 & 64 \\ \(\gamma\) & Center learning rate & 0.5 & 0.5 \\ \(\sigma\) & Marginal factor & 100 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameter setting of LTS-SEIods, including maximum mean discrepancy (MMD) [33], central moment discrepancy (CMD) [39], higher-order moment matching (HOMM) [40], unsupervised domain adaptation by backpropagation (UDAB) [41], deep adaptation networks (DANs) [43], and joint domain alignment (JDA) [44]. The performance upper limit of all methods is the effect when the distributions of the source domain data and target domain data are the same. Additionally, shallow RFFs and deep RFFs are used to confirm the domain alignment effectiveness of these methods. _Shallow layer:_ Shallow RFFs refer to features output by the GAP-1D of the feature extractor. Table 4 presents the average recognition accuracy of the original I/Q signal (no domain adaptation), other domain adaptive methods, the proposed LTS-SEI, and the performance upper limit. Other domain adaptive methods are used for a shallow feature alignment. Evidently, all domain adaptive methods have improvements for A1->A2 and A1->A3. We suppose that although there is a 15-min time span between A1, A2, and A3, they are all signals collected from the same batch, which results in considerable similarity of data distribution. Model A still has a satisfactory predictive ability for data subsets with shorter time spans. Even without using domain adaptive methods, the original I/Q signal can achieve 85% average accuracy. Both UDAB and the proposed LTS-SEI method can reach the upper limit of prediction accuracy. Except for B3, the original I/Q signal and existing domain adaptive methods suffer from poor predictions for B2, B4, and B5. This may be due to changes in space environment or fingerprint features caused by a long time span, resulting in considerable differences in data distribution between B1 and other data subsets. Therefore, Model B loses its predictive and recognition capabilities for Data Subsets B2, B4, and B5. Despite a one-month time span between B1 and B3, LTS-SEI achieves the average accuracy similar to the performance upper limit. For B2, B4, and B5, the advantage of LTS-SEI is more obvious, with an average accuracy of exceeding 96%. Therefore, the existing methods that only align shallow features in the feature space cannot achieve satisfactory recognition results. _Deep layer:_ Deep RFFs refer to output features of the last fully connected layer of the classifier. Table 5 presents the average recognition accuracy of various methods. Compared with the original I/Q signal, center loss (center) can enhance the prediction performance of new data. This indicates that enhancing intraclass consistency can result in correct identification of edge samples in the target domain. HOMM adopts the third-order moment matching loss to train the model described in this study. Compared with the first-order moment matching MMD and CMD, and the second-order moment matching of JDA, HOMM can effectively identify specific emitters in A2, A3, and B6. Therefore, higher-order moments can deeply describe the feature distribution of signals and simplify domain alignment. Additionally, based on the recognition results of A1->A2 and A1->A3, it can be seen that compared with the shallow feature alignment, the existing domain adaptive methods deliver better recognition performances for A2 and A3 when aligning deep