Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (5): 1182-1190.doi: 10.23919/JSEE.2023.000126

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Radar emitter signal recognition method based on improved collaborative semi-supervised learning

Tao JIN(), Xindong ZHANG()   

  1. 1 College of Electronic Science and Engineering, Jilin University, Changchun 130012, China
  • Received:2021-11-16 Online:2023-10-18 Published:2023-10-30
  • Contact: Xindong ZHANG;
  • About author:
    JIN Tao was born in 1997. He received his Master ’s degree from Jilin University in integrated circuit engineering. His research interests are deep learning and radar signal processing.E-mail:

    ZHANG Xindong was born in 1970. She received her Ph.D. degree from Jilin University in microelectronics and solid-electronics. She is a professor in Jilin University. Her research interest is radar signal processing. E-mail:


Rare labeled data are difficult to recognize by using conventional methods in the process of radar emitter recognition. To solve this problem, an optimized cooperative semi-supervised learning radar emitter recognition method based on a small amount of labeled data is developed. First, a small amount of labeled data are randomly sampled by using the bootstrap method, loss functions for three common deep learning networks are improved, the uniform distribution and cross-entropy function are combined to reduce the overconfidence of softmax classification. Subsequently, the dataset obtained after sampling is adopted to train three improved networks so as to build the initial model. In addition, the unlabeled data are preliminarily screened through dynamic time warping (DTW) and then input into the initial model trained previously for judgment. If the judgment results of two or more networks are consistent, the unlabeled data are labeled and put into the labeled data set. Lastly, the three network models are input into the labeled dataset for training, and the final model is built. As revealed by the simulation results, the semi-supervised learning method adopted in this paper is capable of exploiting a small amount of labeled data and basically achieving the accuracy of labeled data recognition.

Key words: emitter signal identification, time series, bootstrap, semi supervised learning, cross entropy function, homogenization, dynamic time warping (DTW)