# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
## 4 Experiments
### Interaction between Transfer Learning Method and Semi-Supervised Learning Method  
The experiment uses the known radar emitter data as labeled samples for auxiliary training, and the unknown radar emitter data as unlabeled samples to be identified. The number of unlabeled samples and labeled samples can be adjusted to verify the interaction between the transfer learning method and the semi-supervised learning method.  
First, we keep the number of labeled samples unchanged, and adjust the number of unlabeled samples to verify the impact of the semi-supervised learning method on the transfer learning method. The results are shown in Figure 3. It can be seen from the experiment results that when the number of unlabeled samples is zero, that is, we only carry out transfer learning without semi-supervised learning, the identification accuracy is 11.3% lower than the optimal identification accuracy.  
When the number of unlabeled samples is slowly increasing, the identification accuracy will also continue to rise, indicating that the unlabeled samples help to make the transfer learning method select high-similarity samples from the unknown radar emitter data; that is, the semi-supervised learning method is positively correlated with the transfer learning method, and has not weakened it. As the number of unlabeled samples increases further, the identification accuracy will gradually stabilize, indicating that the high-similarity samples in the unknown radar emitter data have been completely screened out, and the optimal recognition rate can reach 93.6%.  
Figure 3: Impact of the semi-supervised learning method on the transfer learning method.  
Figure 2: Distribution and change characteristics of the five parameters of pulse amplitude (PA), carrier frequency (CF), pulse width (PW), pulse repetition interval (PRI) and angle of arrival (AOA).  
Secondly, we keep the number of unlabeled samples unchanged, and adjust the number of labeled samples to verify the impact of the transfer learning method on the semi-supervised learning method. The results are shown in Figure 4. It can be seen from the experiment results that when the number of labeled samples is zero, that is, we only carry out semi-supervised learning without transfer learning, the difference between the maximum identification accuracy and the minimum identification accuracy in the classification identification results reaches 17.9%, indicating that the use of semi-supervised learning alone makes the model less stable. When the number of labeled samples is slowly increased, the difference between the maximum identification accuracy and the minimum identification accuracy in the classification identification results will continue to drop to 4.3%, indicating that the transfer learning method helps to make self-correction of the semi-supervised learning method.  
It can be seen from the above experiment results that the semi-supervised and transfer learning method proposed in this paper can comprehensively utilize the information of unlabeled samples and labeled samples. When the number of unlabeled samples is greater than 1000, and the number of labeled samples is greater than 1500, the performance of the model will tend to be stable and achieve the highest identification accuracy. Therefore, in the following we use 1500 known radar emitter samples and 1000 unknown radar emitter samples to train the model for contrast experiments.