Electronics Optics & Control, Volume. 32, Issue 6, 86(2025)

Identification of UAV RF Signals Based on Improved Residual Neural Network

BIAN Ruiqi1, GAO Zhenbin1, YAN Xingwei2,3, and SUN Liting3
Author Affiliations
  • 1School of Electronic Information Engineering, Hebei University of Technology, Tianjin 300000, China
  • 2Tianjin Institute of Advanced Technology, Tianjin 300000, China
  • 3School of Electronic Science, National University of Defense Technology, Changsha 410000, China
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    References(5)

    [1] [1] BOUCHER P. Domesticating the drone: the demilitarisation of unmanned aircraft for civil markets[J]. Science and Engineering Ethics, 2015, 21(6): 1393-1412.

    [2] [2] WARGO C A, CHURCH G C, GLANEUESKI J, et al. Unmanned Aircraft Systems (UAS) research and future analysis [C]//2014 IEEE Aerospace Conference. Big Sky: IEEE, 2014: 1-16.

    [13] [13] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.

    [14] [14] OUYANG D, HE S, ZHANG G, et al. Efficient multi-scale attention module with cross-spatial learning[C]//2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Rhodes Island: IEEE, 2023: 1-5.

    [15] [15] LIU Y, SHAO Z, HOFFMANN N. Global attention mechanism: retain information to enhance channel-spatial interactions [EB/OL]. (2021-12-10)[2024-05-31]. https://doi.org/10.48550/arXiv.2112.05561.

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    BIAN Ruiqi, GAO Zhenbin, YAN Xingwei, SUN Liting. Identification of UAV RF Signals Based on Improved Residual Neural Network[J]. Electronics Optics & Control, 2025, 32(6): 86

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    Paper Information

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    Received: May. 31, 2024

    Accepted: Jun. 12, 2025

    Published Online: Jun. 12, 2025

    The Author Email:

    DOI:10.3969/j.issn.1671-637x.2025.06.014

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