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Radar target recognition based on few-shot learning

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Abstract

With the continuous development of target recognition technology, people pay more and more attention to the cost of sample generation, tag addition and network training. Active learning can choose as few samples as possible to achieve a better recognition effect. In this paper, a small number of the simulation generated radar cross-section time series are selected as the training data, combined with the least confidence and edge sampling, a sample selection method based on few-shot learning is proposed. The effectiveness of the method is verified by the target type recognition test in multi time radar cross-section time series. Using the algorithm in this paper, 10 kinds of trajectory data are selected from all 19 kinds of trajectory data, and the training model is tested, which can achieve similar results with 19 kinds of trajectory data training model. Compared with the random selection method, the accuracy is improved by 4–10% in different time lengths.

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Funding

Partial financial support was received from National Natural Science Foundation of China (No. 61871283), the Foundation of Pre-Research on Equipment of China (No. 61400010304) and Major Civil-Military Integration Project in Tianjin, China (No. 18ZXJMTG00170).

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Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, Tianjin, China

    Yue Yang, Zhuo Zhang, Yang Li & Chengang Lv

  2. China Academy of Launch Vehicle Technology, Beijing, China

    Wei Mao

  3. College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China

    Yang Li

Authors
  1. Yue Yang
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  2. Zhuo Zhang
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  3. Wei Mao
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  4. Yang Li
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  5. Chengang Lv
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Contributions

Conceptualization, methodology, formal analysis and investigation: YY and ZZ; writing—original draft preparation: YY; supervision: YL, WM and CL; resources: WM and CL.

Corresponding author

Correspondence to Yang Li.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Yang, Y., Zhang, Z., Mao, W. et al. Radar target recognition based on few-shot learning. Multimedia Systems 29, 2865–2875 (2023). https://doi.org/10.1007/s00530-021-00832-3

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