Volume 12 Issue 4
Aug.  2023
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Article Navigation >  Journal of Radars  > 2023  >  12(4): 882-891
ZHANG Shunsheng, CHEN Shuang, CHEN Xiaoying, et al. Active deception jamming recognition method in multimodal radar based on small samples[J]. Journal of Radars, 2023, 12(4): 882–891. doi: 10.12000/JR23104
Citation: ZHANG Shunsheng, CHEN Shuang, CHEN Xiaoying, et al. Active deception jamming recognition method in multimodal radar based on small samples[J]. Journal of Radars, 2023, 12(4): 882–891. doi: 10.12000/JR23104
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Active Deception Jamming Recognition Method in Multimodal Radar Based on Small Samples

DOI: 10.12000/JR23104
  • 1.

    Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Ministries Foundation
More Information
  • Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn
  • Received Date: 2023-06-11
  • Rev Recd Date: 2023-07-22
    • Available Online: 2023-07-25
  • Publish Date: 2023-08-15
    Abstract
  • Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.

     

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