# A Long Time Span-Specific Emitter Identification Method Based on Unsupervised Domain Adaptation
## 5 Experimental Results and Discussion
### Parameters Setting  
An appropriate parameters setting is crucial for training a superior model. Each module of the LTS-SEI framework involves multiple hyperparameters, and we must explain these parameters in detail. Table 3 presents the values of nine hyperparameters that need to be set during the training process. Additionally, we need to explain the following four points about the parameters setting:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Subset** & **Specific Emitter 1** & **Specific Emitter 2** \\ \hline B7 & 14 October 2021 & 23 October 2021 \\ B8 & 6 December 2021 & 3 December 2021 \\ B9 & 11 February 2022 & 13 February 2022 \\ B10 & 8 March 2022 & 11 March 2022 \\ B11 & 10 May 2022 & 14 May 2022 \\ B12 & 15 June 2022 & 16 June 2022 \\ B13 & 7 August 2022 & 3 August 2022 \\ B14 & 21 September 2022 & 28 September 2022 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._1. Learning rate \(\mu\): Consistent with traditional models, \(\mu\) is used to control the learning rate of the feature extractor, classifier, and domain discriminator. For Dataset A, which has a short time span, Model A is easy to train. The learning rate of Model A is set to the commonly used value of 0.01. For Dataset B, which has a long time span, the learning rate of Model B gradually decreases with the increase in iterations to achieve fast convergence. \(\mu\) is defined as \[\mu=\frac{1}{(1+\frac{epoch}{E})}\] (25) where \(epoch\) represents the current number of iterations of the model and \(E\) the total number of iterations of the model.
2. Reversal Scalar \(\lambda\):\(\lambda\) is a weight scalar used to control negative gradient. Similar to (1), we set the reversal scalar of Model A to a constant. For Model B, as \(\mu\) gradually decreases, \(\lambda\) is set as an increasing function with respect to number of iterations in a bid to encourage the feature extractor to continuously learn domain-invariant features. \(\lambda\) is represented as \[\lambda=\frac{2}{1+e^{-10.\frac{epoch}{E}}}-1\] (26)
3. Epoch \(E\): After multiple experimental verifications, we determined that for Data Subsets B3, B6, and B12, Model B can achieve nearly 100% accuracy through only 100 iterations. Therefore, for these three data subsets, the number of iterations of the model are set to 100. The number of iterations for other data subsets are 300.
4. Center Loss weight \(\alpha\) and HOMM3 Loss weight \(\beta\): We set the weight factors of the two loss functions to the same value to quickly determine suitable hyperparameters \(\alpha\) and \(\beta\). The weight range is [0, 1] and the step size is 0.001. We experimentally determined that the domain adaptation ability of the model decreases when the two weight factors are small. The model may overfitting when the two weight factors are large. Model A can maintain a high recognition accuracy on Dataset A when both  
Figure 7: Identification models of two datasets: (**a**) Dataset A and (**b**) Dataset B.  
weight factors are 0.1. Model B can maintain a high recognition accuracy on Dataset B when both weight factors are 0.01.