Volume 11 Issue 4
Aug.  2022
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Article Navigation >  Journal of Radars  > 2022  >  11(4): 713-727
TAN Kaiwen, ZHANG Limin, YAN Wenjun, et al. A semi-supervised emitter identification method for imbalanced category[J]. Journal of Radars, 2022, 11(4): 713–727. doi: 10.12000/JR22043
Citation: TAN Kaiwen, ZHANG Limin, YAN Wenjun, et al. A semi-supervised emitter identification method for imbalanced category[J]. Journal of Radars, 2022, 11(4): 713–727. doi: 10.12000/JR22043
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A Semi-supervised Emitter Identification Method for Imbalanced Category

DOI: 10.12000/JR22043

  • Institute of Information Fusion, Naval Aviation University, Yantai 264001, China

Funds:  The National Natural Science Foundation of China (91538201)
More Information
  • Corresponding author: ZHANG Limin, iamzlm@163.com; YAN Wenjun, wj_yan@foxmail.com
  • Received Date: 2022-03-11
  • Accepted Date: 2022-04-28
  • Rev Recd Date: 2022-05-05
    • Available Online: 2022-05-09
  • Publish Date: 2022-05-17
    Abstract
  • This paper proposes an SEI method based on cost-sensitive learning and semisupervised generative adversarial networks to address the problem of incomplete sample labels and imbalanced data category distribution in Specific Emitter Identification (SEI), which leads to a decline in inaccuracy. Through semisupervised training, the method optimizes the network parameters of the generator and discriminator, adds a multiscale topological block to ResNet to fuse the multi-dimensional resolution features of the time-domain signal, and attributes additional labels to the generated samples to directly use the discriminator to complete the classification. Simultaneously, a cost-sensitive loss is designed to alleviate the imbalance of gradient propagation caused by the dominant samples and improve the recognition performance of the classifier on the class-imbalanced dataset. The experimental results on four types of imbalanced datasets show that in the presence of 40% unlabeled samples, the average recognition accuracy for five emitters is improved by 5.34% and 2.69%, respectively, compared with the cross-entropy loss and focus loss. This provides a new idea for solving the problem of SEI under the conditions of insufficient data labels and an unbalanced distribution of data.

     

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