Telecommunications Science ›› 2023, Vol. 39 ›› Issue (10): 15-28.doi: 10.11959/j.issn.1000-0801.2023182

• Research and Development • Previous Articles    

A semi-supervised transfer learning recognition method for radar compound jamming under small samples

Jinqiang WANG1, Minhong SUN1, Xianghong TANG1, Zhaoyang QIU1, Deguo ZENG2   

  1. 1 School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
    2 China Aerospace Science and Technology Group 8511 Research Institute, Nanjing 210007, China
  • Revised:2023-09-06 Online:2023-10-01 Published:2023-10-01
  • Supported by:
    The National Natural Science Foundation of China(61901149);The National Defense Characteristic Discipline Development Project(JCKY2019415D002)

Abstract:

Aiming at the problem that more and more kinds of radar compound jamming signals and too few training samples were difficult to make the deep learning model reach the optimal state, a semi-supervised transfer learning recognition method for radar compound jamming under small samples was proposed, which solved the problem of low network training accuracy caused by the difficulty in obtaining labeled samples through unlabeled samples.The feature extractor and classifier obtained after pre-training of single jamming data set were transferred to small-scale compound jamming data set, and the model was fine-tuning by using weight imprinting and semi-supervised learning.The model parameters were optimized by the proposed nearest neighbor correlation loss nearest neighbor correlation loss (NNCL).The experimental results show that the recognition accuracy of the model can reach 93.20% when the jamming-to-noise ratio is 10 dB and there are only 5 labeled samples of the new class of compound jamming signals.

Key words: radar anti-jamming, compound jamming recognition, transfer learning, weight imprinting, semi-supervised learning

CLC Number: 

No Suggested Reading articles found!