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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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    • References(29)
  • [1]
    崔国龙, 余显祥, 魏文强, 等. 认知智能雷达抗干扰技术综述与展望[J]. 雷达学报, 2022, 11(6): 974–1002. doi: 10.12000/JR22191.

    CUI Guolong, YU Xianxiang, WEI Wenqiang, et al. An overview of antijamming methods and future works on cognitive intelligent radar[J]. Journal of Radars, 2022, 11(6): 974–1002. doi: 10.12000/JR22191.
    [2]
    张顺生, 陈爽, 陈晓莹, 等. 面向小样本的多模态雷达有源欺骗干扰识别方法[J]. 雷达学报, 2023, 12(4): 882–891. doi: 10.12000/JR23104.

    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.
    [3]
    郭文杰, 吴振华, 曹宜策, 等. 多域浅层特征引导下雷达有源干扰多模态对比识别方法[J]. 雷达学报(中英文), 2024, 13(5): 1004–1018. doi: 10.12000/JR24129.

    GUO Wenjie, WU Zhenhua, CAO Yice, et al. Multidomain characteristic-guided multimodal contrastive recognition method for active radar jamming[J]. Journal of Radars, 2024, 13(5): 1004–1018. doi: 10.12000/JR24129.
    [4]
    WANG Zan, GUO Zhengwei, SHU Gaofeng, et al. Radar jamming recognition: Models, methods, and prospects[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 3315–3343. doi: 10.1109/JSTARS.2024.3522951.
    [5]
    LECUN Y, BENGIO Y, and HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539.
    [6]
    ZHANG Haoyu, YU Lei, CHEN Yushi, et al. Fast complex-valued CNN for radar jamming signal recognition[J]. Remote Sensing, 2021, 13(15): 2867. doi: 10.3390/rs13152867.
    [7]
    LIU Qiang and ZHANG Wei. Deep learning and recognition of radar jamming based on CNN[C]. The 12th International Symposium on Computational Intelligence and Design, Hangzhou, China, 2019: 208–212. doi: 10.1109/ISCID.2019.00054.
    [8]
    CAI Yuan, SHI Kai, SONG Fei, et al. Jamming pattern recognition using spectrum waterfall: A deep learning method[C]. The 5th International Conference on Computer and Communications, Chengdu, China, 2019: 2113–2117. doi: 10.1109/ICCC47050.2019.9064207.
    [9]
    WANG Jingyi, DONG Wenhao, SONG Zhiyong, et al. Identification of radar active interference types based on three-dimensional residual network[C]. The 3rd International Academic Exchange Conference on Science and Technology Innovation, Guangzhou, China, 2021: 167–172. doi: 10.1109/IAECST54258.2021.9695564.
    [10]
    陈思伟, 崔兴超, 李铭典, 等. 基于深度CNN模型的SAR图像有源干扰类型识别方法[J]. 雷达学报, 2022, 11(5): 897–908. doi: 10.12000/JR22143.

    CHEN Siwei, CUI Xingchao, LI Mingdian, et al. SAR image active jamming type recognition based on deep CNN model[J]. Journal of Radars, 2022, 11(5): 897–908. doi: 10.12000/JR22143.
    [11]
    QU Qizhe, WEI Shunjun, LIU Shan, et al. JRNet: Jamming recognition networks for radar compound suppression jamming signals[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15035–15045. doi: 10.1109/TVT.2020.3032197.
    [12]
    LV Qinzhe, QUAN Yinghui, FENG Wei, et al. Radar deception jamming recognition based on weighted ensemble CNN with transfer learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5107511. doi: 10.1109/TGRS.2021.3129645.
    [13]
    LV Qinzhe, QUAN Yinghui, SHA Minghui, et al. Deep neural network-based interrupted sampling deceptive jamming countermeasure method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 9073–9085. doi: 10.1109/JSTARS.2022.3214969.
    [14]
    ZHANG Jiaxiang, LIANG Zhennan, ZHOU Chao, et al. Radar compound jamming cognition based on a deep object detection network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(3): 3251–3263. doi: 10.1109/TAES.2022.3224695.
    [15]
    ZHU Xuan, WU Hao, HE Fangmin, et al. YOLO-CJ: A lightweight network for compound jamming signal detection[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(5): 6807–6821. doi: 10.1109/TAES.2024.3406491.
    [16]
    MA Xu, DAI Xiyang, BAI Yue, et al. Rewrite the stars[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 5694–5703. doi: 10.1109/CVPR52733.2024.00544.
    [17]
    LI Hulin, LI Jun, WEI Hanbing, et al. Slim-neck by GSConv: A lightweight-design for real-time detector architectures[J]. Journal of Real-Time Image Processing, 2024, 21(3): 62. doi: 10.1007/s11554-024-01436-6.
    [18]
    YU Hongyuan, WAN Cheng, DAI Xiyang, et al. Real-time image segmentation via hybrid convolutional-transformer architecture search[EB/OL]. https://arxiv.org/abs/2403.10413, 2025.
    [19]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. The 31st International Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 6000–6010.
    [20]
    ZHANG Haoyang, WANG Ying, DAYOUB F, et al. VarifocalNet: An IOU-aware dense object detector[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 8510–8519. doi: 10.1109/CVPR46437.2021.00841.
    [21]
    REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031.
    [22]
    LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37. doi: 10.1007/978-3-319-46448-0_2.
    [23]
    LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]. IEEE International Conference on Computer Vision, Venice, Italy, 2017: 2999–3007. doi: 10.1109/ICCV.2017.324.
    [24]
    ZHAO Yi’an, LV Wenyu, XU Shangliang, et al. DETRS beat YOLOs on real-time object detection[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 16965–16974. doi: 10.1109/CVPR52733.2024.01605.
    [25]
    翟永杰, 田济铭, 陈鹏晖, 等. 基于YOLOv8n-Aerolite的轻量化蝴蝶兰种苗目标检测算法[J]. 农业工程学报, 2025, 41(4): 220–229. doi: 10.11975/j.issn.1002-6819.202408192.

    ZHAI Yongjie, TIAN Jiming, CHEN Penghui, et al. Algorithm for the target detection of phalaenopsis seedlings using lightweight YOLOv8n-Aerolite[J]. Transactions of the Chinese Society of Agricultural Engineering, 2025, 41(4): 220–229. doi: 10.11975/j.issn.1002-6819.202408192.
    [26]
    WANG C Y, YEH I H, and LIAO H Y M. YOLOv9: Learning what you want to learn using programmable gradient information[C]. The 18th European Conference on Computer Vision, Milan, Italy, 2025: 1–21. doi: 10.1007/978-3-031-72751-1_1.
    [27]
    WANG Ao, CHEN Hui, LIU Lihao, et al. YOLOv10: Real-time end-to-end object detection[C]. The 38th International Conference on Neural Information Processing Systems, Vancouver, Canada, 2024: 3429.
    [28]
    ZHUO Shulong, BAI Hao, JIANG Lifeng, et al. SCL-YOLOv11: A lightweight object detection network for low-illumination environments[J]. IEEE Access, 2025, 13: 47653–47662. doi: 10.1109/ACCESS.2025.3550947.
    [29]
    TIAN Yunjie, YE Qixiang, and DOERMANN D. YOLOv12: Attention-centric real-time object detectors[EB/OL]. https://arxiv.org/abs/2502.12524, 2025.
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