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WANG Jiaxiang, MENG Jin, LI Wei, et al. YOLO-S3: a lightweight network for radar composite jamming signal recognition[J]. Journal of Radars, in press. doi: 10.12000/JR25080
Citation: WANG Jiaxiang, MENG Jin, LI Wei, et al. YOLO-S3: a lightweight network for radar composite jamming signal recognition[J]. Journal of Radars, in press. doi: 10.12000/JR25080
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YOLO-S3: A Lightweight Network for Radar Composite Jamming Signal Recognition

DOI: 10.12000/JR25080 CSTR: 32380.14.JR25080
  • 1.

    National Key Laboratory of Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China

  • 2.

    School of Electrical Engineering and Automation, Wuhan University, Wuhan 430073, China

Funds:  The National Science Foundation of China (52177012), the National Fund for Distinguished Young Scholars (52025072), the Independent Research and Development Program of NUE (2025500080)
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  • Corresponding author: ZHANG Jiahao, jiahao.z@hotmail.com
  • Received Date: 2025-04-29
  • Rev Recd Date: 2025-08-14
    • Available Online: 2025-08-16
    Abstract
  • To enhance the jamming recognition capabilities of radars in complex electromagnetic environments, this study proposes YOLO-S3, a lightweight network for recognizing composite jamming signals. YOLO-S3 is characterized by three core attributes: Smartness, slimness, and high speed. Initially, a technical approach based on visual detection algorithms is introduced to identify 2D time-frequency representations of jamming signals. An image dataset of composite jamming signals is constructed using signal modeling, simulation technology, and the short-time Fourier transform. Next, the backbone and neck networks of YOLOv8n are restructured by integrating StarNet and SlimNeck, and a Self-Attention Detect Head (SADH) is designed to enhance feature extraction. These modifications result in a lightweight network without compromising recognition accuracy. Finally, the network’s performance is validated through ablation and comparative experiments. Results show that YOLO-S3 features a highly lightweight network design. When the signal-to-jamming ratio varies from −10 to 0 dB and the Signal-to-Noise Ratio (SNR) is ≥0 dB, the network achieves an impressive average recognition accuracy of 99.5%. Even when the SNR decreases to −10 dB, it maintains a robust average recognition accuracy of 95.5%, exhibiting strong performance under low SNR conditions. These findings provide a promising solution for the real-time recognition of composite jamming signals on resource-constrained platforms such as airborne radar signal processors and portable electronic devices.

     

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