Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 279-286.doi: 10.11896/jsjkx.240600073

• Computer Network • Previous Articles     Next Articles

Specific Emitter Identification Based on Progressive Self-training Open Set Domain Adaptation

ZHANG Taotao, XIE Jun, QIAO Pingjuan   

  1. School of Command and Control Engineering, Army Engineering University of the PLA, Nanjing 210007, China
  • Received:2024-06-11 Revised:2024-09-12 Published:2025-07-17
  • About author:ZHANG Taotao,born in 1992,postgra-duate.His main research interests include wireless network and specific emitter identification.
    XIE Jun,born in 1973,Ph.D,professor,Ph.D supervisor.His main research interests include wireless networks and intelligent network management.

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Abstract: Aiming at the problem that the specific emitter identification model trained in the closed set scene will suffer from the degradation of the known class recognition performance and the error of the new class recognition when deployed in the environmental conditions of the specific emitter identification containing the new class,this paper proposes an specific emitter identification method based on open set domain adaptation in the noise change scene.The maximum and minimum thresholds are used to distinguish the known class and the unknown class,and a target classifier is trained by a progressive self-training method for testing the scene.An unknown classification of the target classifier fit the feature distribution of multiple unknown classes at the same time may lead to the problem of boundary confusion of the learned known and unknown feature distribution.Based on this,a multi-center loss is proposed to increase the compactness within the unknown class of the target known class and the distinguish ability between classes,which can improve the accuracy of the target classifier.At the same time,in order to reduce the problem of fingerprint feature offset caused by noise between the source domain and the target domain,a prototype-to-prototype contrastive learning is used to learn domain invariant features.Six groups of experiments are carried out on the public data set.The HOS index of the proposed method in five groups is better than other methods,and the HOS reaches 93.8 % in the task of 10dB-8dB.The experimental results show the effectiveness of the proposed method.

Key words: Specific emitter identification, Open set domain adaptation, Self-training, Center loss, Contrastive learning

CLC Number: 

  • TP391
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