# An Unknown Radar Emitter Identification Method Based on Semi-Supervised and Transfer Learning
[PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; [EMAIL_ADDRESS] (G.W. ); [EMAIL_ADDRESS] (Z.L. );  
[EMAIL_ADDRESS] (R.F. ); [EMAIL_ADDRESS] (N.C.);  
[EMAIL_ADDRESS] (N.T.) [PERSON]  
1 State Key Lab. of Complex Electromagnetic Environment Effects on Electronics and Information System;  
Luoyang 471003, China; guoliangwangcemee@163.  
with the complex electromagnetic environment and varied signal styles, [PERSON] et al. [9] proposed a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. In the past battlefields, the types of radar emitters are single and limited, and the above methods can solve the problem of radar emitter identification well. However, with the increasing number and variety of radar emitters, many unknown emitters will appear in the future battlefield. As time goes by and the location changes, the current identification methods will face two problems. First, the training data and testing data no longer satisfy the same-distribution hypothesis, resulting in a decrease in the classification performance of the machine learning model. Second, the number of available labeled samples for unknown emitters is seriously insufficient, which may lead to over-fitting of the machine learning model. In recent years, the transfer learning methods [10] and the semi-supervised learning methods [11] have gained more and more attention. Transfer learning does not require that the training data and testing data meet the conditions of the same distribution in the model training process, and utilizes the knowledge in a large number of known samples for training, which is good for cross-domain learning. However, the transferring of a large amount of irrelevant information will also cause negative transfer, which reduces the effect of identification.