Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (12): 1666-1674.doi: 10.16183/j.cnki.jsjtu.2022.041

Special Issue: 《上海交通大学学报》2022年“电子信息与电气工程”专题

• Electronic Information and Electrical Engineering • Previous Articles     Next Articles

A Few-Shots OFDM Target Augmented Identification Method Based on Transfer Learning

TANG Zeyu1, ZOU Xiaohu1, LI Pengfei1, ZHANG Wei1,2(), YU Jiaqi3, ZHAO Yaodong1   

  1. 1. Science and Technology on Electronic Information Control Laboratory, Chengdu 610036, China
    2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
    3. School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100091, China
  • Received:2022-02-25 Online:2022-12-28 Published:2023-01-05
  • Contact: ZHANG Wei


Under the few-shots condition caused by non-cooperative scenes, robust extraction of communication emitter features and accurate identification of targets are the difficulties and hotspots of current research. Aimed at the problem of emitter identification under the few-shots condition of orthogonal frequency division multiplexing (OFDM) signals, this paper proposes a non-cooperative target identification method based on phase/time domain flipping data augmentation and source domain instance-based transfer learning. The data set is expanded by different domain flipping data augmentation methods, and the improved residual network is applied to achieve the purpose of promoting the identification rate of the OFDM emitter. Then, transfer learning is introduced to strengthen the generalization ability of the identification model. The experimental results show that the data augmentation method can significantly improve the OFDM emitter identification rate under the few-shots condition. Furthermore, the transfer learning method accelerates the convergence speed, slightly increases the recognition rate, and improves robustness of the model.

Key words: orthogonal frequency division multiplexing (OFDM), few-shots identification, data augmentation, transfer learning, deep learning, target identification

CLC Number: