# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 1 Introduction
Additionally, the authors also transferred the model trained using the proposed method under an additive white Gaussian noise channel to a multipath channel. Ref. [36] proposed an SEI method based on deep adversarial \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**RFFs Extraction Method** & **Work** & **Extracted RFFs/Models** & **Performance** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{Direct Extraction} & [6] & I/Q Imbalance & SNR \(\geq\) 28 dB, Pcc \(\approx\) 100\% & & \multirow{4}{*}{\begin{tabular}{} \end{tabular} } \\ \cline{2-4}  & [7] & I/Q Imbalance & Unknown SNR, Pcc \(\approx\) 100\% & & \\ \cline{2-4}  & [8] & I/Q Constellation Impairments & SNR \(\geq\) 15 dB, Pcc \(\geq\) 98\% & & \\ \cline{2-4}  & [9] & CFO & Only RFFs extraction, no SEI & & \\ \hline \multirow{4}{*}{Domain Transformation} & [10] & Hilbert Huang Transform & SNR \(\geq\) 0 dB, Pcc \(\geq\) 80\% & & \\ \cline{2-4}  & [11] & VMD-SF & SNR \(\geq\) \(-\)2 dB, Pcc \(\geq\) 80\% & & \\ \cline{2-4}  & [12] & tvr-EMD & SNR \(\geq\) 16 dB, Pcc \(\geq\) 85\% & & \\ \cline{2-4}  & [13] & MApEn & SNR = 15 dB, Pcc \(\approx\) 95.65\% & & \\ \cline{2-4}  & [14] & Joint Wavelet Packet Analysis & SNR \(\geq\) 0 dB, Pcc \(\geq\) 83\% & & \\ \hline \multirow{4}{*}{Deep Learning} & [18] & Inception-Residual Neural Network & SNR \(\geq\) 9 dB, Pcc \(\geq\) 92\% & & \\ \cline{2-4}  & [19] & MSCNN & SNR \(\geq\) 30 dB, Pcc \(\geq\) 97\% & & \\ \cline{2-4}  & [20] & CNN & Achieves a Gain of about 3 dB & Strong RFFs extraction ability; end-to-end & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\ \cline{2-4}  & [21] & CVNN + Network Compression & At High SNR, Pcc \(\approx\) 100\% & & \\ \cline{2-4}  & [22] & TDGTAN & Unknown SNR, Pcc \(\approx\) 100\% & & \\ \hline \hline \end{tabular} Note: “Pcc” is the abbreviation for percentage correct classification.  
\end{table} 
Table 1: Works Related to RFF Extraction Methods. domain adaptation (DADA) to solve the problem that DL-based SEI methods are limited by the training scene and have poor generalization ability in the complex scene. DADA integrates deep neural networks into the domain adaptation problem of transfer learning. It can effectively enhance the recognition performance of the network for unlabeled samples under different conditions. Ref. [37] studied a deep multicore metric domain adaptation algorithm appropriate for underwater target recognition, which introduced the divergence deviation metric and multicore technology. This algorithm can utilize a large amount of sample data in the source domain to assist in training classifiers in the target domain. The robustness or adaptability of the above-mentioned methods was tested by manually adding noise. However, the actual environment may not match the modeled channel, particularly the space environment in which the satellite downlink signals are located. Therefore, the effectiveness of the above-mentioned methods still needs to be tested for long time span-specific emitter signals collected in real-world scenarios. Recently, researchers have been deeply interested in the task of cross-modal object classification in the field of computer vision and have proposed various domain adaptive algorithms [33; 38; 39; 40; 41; 42; 43; 44; 45]. Domain adaptation is a subset of transfer learning. It can be used to migrate a model trained from the source domain to the target domain and enhance identification performance. Accordingly, we introduce domain adaptation to solve the long time span SEI problem. We classify the initial collected signals as source domain samples and the subsequent collected signals of the same type as target domain samples. A certain time span exists between the source domain samples and target domain samples, which may be short or long. In this study, we propose a long time span SEI method based on unsupervised domain adaptation and verify it on real datasets. Specifically, our work and contributions are as follows:  
1.