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Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network

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Abstract

Intra-pulse modulation recognition of radar signals plays an important role in the field of electronic warfare. In this paper, a multi-feature random matching fusion (MFRMF) network is proposed to deal with the recognition technology of radar signals’ intra-pulse modulation at a low signal-to-noise ratio (SNR). First, we extract 12 traditional parameter features of radar signals and screen out 7 more important features. Next, we analyze and extract the Time–frequency images. Finally, the MFRMF network with the idea of residual learning, self-attention mechanism, and random matching algorithm is adopted to perform feature learning and identify the intra-pulse modulation type of radar signals. Simulation results demonstrate that MFRMF can effectively reduce the interference of noise on signal classification and improve recognition accuracy at a low SNR. It can classify 10 kinds of radar signals, and the overall recognition accuracy achieves 90.6% and 95.4% when the SNR is − 8 dB and − 6 dB, respectively.

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Data Availability

The experiment data will be made available upon reasonable request for academic use and within the limitations of the provided informed consent by the corresponding author upon acceptance.

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Acknowledgements

This paper is funded by Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China. The authors are grateful to the anonymous referees for their valuable comments and suggestions that improved this paper.

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Authors and Affiliations

  1. Department of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China

    Yanping Liao & Fan Jiang

  2. Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin, China

    Yanping Liao

  3. Department of Computer Science and Technology, Harbin Engineering University, Harbin, 150001, China

    Jinli Wang

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  1. Yanping Liao
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Correspondence to Fan Jiang.

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Appendix

Appendix

The calculation formulas of 12 features extracted from radar signals in the three domains are shown in Table 10.

Table 10 Principles of 12 traditional traits
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Liao, Y., Jiang, F. & Wang, J. Intra-pulse modulation recognition of radar signals based on multi-feature random matching fusion network. J Supercomput 79, 6422–6451 (2023). https://doi.org/10.1007/s11227-022-04902-9

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