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PU Wei, WU Yuheng, SONG Yue, et al. A scattering feature-enhanced network for SAR deceptive jamming discrimination[J]. Journal of Radars, in press. doi: 10.12000/JR25275
Citation: PU Wei, WU Yuheng, SONG Yue, et al. A scattering feature-enhanced network for SAR deceptive jamming discrimination[J]. Journal of Radars, in press. doi: 10.12000/JR25275
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A Scattering Feature-Enhanced Network for SAR Deceptive Jamming Discrimination

DOI: 10.12000/JR25275 CSTR: 32380.14.JR25275

  • School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds:  The National Natural Science Foundation of China (U25B200901)
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  • Corresponding author: PU Wei, puwei@uestc.edu.cn
  • Received Date: 2025-12-23
  • Rev Recd Date: 2026-05-24
    • Available Online: 2026-06-05
    Abstract
  • Synthetic aperture radar (SAR) enables round-the-clock high-resolution imaging under all weather conditions, thereby playing a vital role in both military domains (e.g., surveillance, reconnaissance, air defense, and missile defense) and civilian domains (e.g., disaster monitoring).. However, advancements in electronic countermeasure technologies have led to the development of radar jammers that generate deceptive jamming with false targets in SAR imagery. This seriously undermines the interpretation of SAR images and real-time decision-making. To tackle these issues, this study proposes a scattering feature–enhanced vision Transformer-based network (SF-ViT) to discriminate deceptive jamming using SAR false targets, which leverages the electromagnetic scattering mechanisms of targets. By targeting the azimuth distribution disparity of echoes caused by the fixed spatial positions of jammers and the scattering feature discrepancy induced by variations in template configurations and signal parameters, the network first highlights the differences between real and false targets in the image domain using a shallow feature enhancement module. Subsequently, it extracts and classifies high-dimensional semantic features through a lightweight hybrid convolutional–ViT network. Experimental validation on the SAR false-target deceptive jamming dataset built in this study indicates that the proposed network attains an average discrimination accuracy of 94.97% under diverse signal-to-noise ratio conditions and requires fewer parameters, making it easy to deploy on edge devices. In addition, ablation experiments demonstrate that the proposed scattering feature enhancement module can be integrated with traditional models, further enhancing the discrimination accuracy of SAR false-target deceptive jamming.

     

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    • References(48)
  • [1]
    武俊杰, 杨建宇, 李中余, 等. 双基地SAR成像处理方法综述[J]. 雷达学报(中英文), 2025, 14(5): 1115–1141. doi: 10.12000/JR25067.

    WU Junjie, YANG Jianyu, LI Zhongyu, et al. Review of bistatic synthetic aperture radar imaging methods[J]. Journal of Radars, 2025, 14(5): 1115–1141. doi: 10.12000/JR25067.
    [2]
    保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2005.

    BAO Zheng, XING Mengdao, and WANG Tong. Radar Imaging Technology[M]. Beijing: Publishing House of Electronics Industry, 2005.
    [3]
    CONDLEY C J. Some system considerations for electronic countermeasures to synthetic aperture radar[C]. IEE Colloquium on Electronic Warfare Systems, London, UK, 1991: 8/1–8/7.
    [4]
    王雪松, 刘建成, 张文明, 等. 间歇采样转发干扰的数学原理[J]. 中国科学 E 辑 信息科学, 2006, 36(8): 891–901. doi: 10.1360/zf2006-36-8-891.

    WANG Xuesong, LIU Jiancheng, ZHANG Wenming, et al. Mathematical principle of intermittent sampling and forward interference[J]. Science in China Series E: Information Sciences, 2006, 36(8): 891–901. doi: 10.1360/zf2006-36-8-891.
    [5]
    吴晓芳, 王雪松, 卢焕章. 对SAR的间歇采样转发干扰研究[J]. 宇航学报, 2009, 30(5): 2043–2048, 2072. doi: 10.3873/j.issn.1000-1328.2009.05.050.

    WU Xiaofang, WANG Xuesong, and LU Huanzhang. Study of intermittent sampling repeater jamming to SAR[J]. Journal of Astronautics, 2009, 30(5): 2043–2048, 2072. doi: 10.3873/j.issn.1000-1328.2009.05.050.
    [6]
    胡东辉, 吴一戎. 合成孔径雷达散射波干扰研究[J]. 电子学报, 2002, 30(12): 1882–1884. doi: 10.3321/j.issn:0372-2112.2002.12.040.

    HU Donghui and WU Yirong. The scatter-wave jamming to SAR[J]. Acta Electronica Sinica, 2002, 30(12): 1882–1884. doi: 10.3321/j.issn:0372-2112.2002.12.040.
    [7]
    王盛利, 于立, 倪晋鳞, 等. 合成孔径雷达的有源欺骗干扰方法研究[J]. 电子学报, 2003, 31(12): 1900–1902. doi: 10.3321/j.issn:0372-2112.2003.12.035.

    WANG Shengli, YU Li, NI Jinlin, et al. A study on the active deception jamming to SAR[J]. Acta Electronica Sinica, 2003, 31(12): 1900–1902. doi: 10.3321/j.issn:0372-2112.2003.12.035.
    [8]
    WANG Wenjing, WU Junjie, PEI Jifang, et al. Deception-jamming localization and suppression via configuration optimization for multistatic SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5232016. doi: 10.1109/TGRS.2022.3189409.
    [9]
    LIU Yongcai, WANG Wei, PAN Xiaoyi, et al. Inverse omega-K algorithm for the electromagnetic deception of synthetic aperture radar[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9(7): 3037–3049. doi: 10.1109/JSTARS.2016.2543961.
    [10]
    SHI Xiaoran, ZHOU Feng, ZHAO Bo, et al. Deception jamming method based on micro-Doppler effect for vehicle target[J]. IET Radar, Sonar & Navigation, 2016, 10(6): 1071–1079. doi: 10.1049/iet-rsn.2015.0371.
    [11]
    ZHAO Bo, HUANG Lei, ZHOU Feng, et al. Performance improvement of deception jamming against SAR based on minimum condition number[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(3): 1039–1055. doi: 10.1109/JSTARS.2016.2614957.
    [12]
    刘宁, 赵博, 黄磊. 单通道SAR抗欺骗干扰方法[J]. 雷达学报, 2019, 8(1): 73–81. doi: 10.12000/JR18072.

    LIU Ning, ZHAO Bo, and HUANG Lei. Anti-deceptive jamming methods based on single-channel synthetic aperture radar[J]. Journal of Radars, 2019, 8(1): 73–81. doi: 10.12000/JR18072.
    [13]
    LOU Mingyue, YANG Jianyu, LI Zhongyu, et al. Joint optimal and adaptive 2-D spatial filtering technique for FDA-MIMO SAR deception jamming separation and suppression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5238414. doi: 10.1109/TGRS.2022.3221786.
    [14]
    ZHOU Bo, LI Yifan, and WANG Zuhang. A fast startup crystal oscillator with digital SAR-AFC based two-step injection[J]. Chinese Journal of Electronics, 2024, 33(5): 1147–1153. doi: 10.23919/cje.2023.00.043.
    [15]
    BANG Huang, WANG Wenqin, ZHANG Shunsheng, et al. FDA-based space-time-frequency deceptive jamming against SAR imaging[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(3): 2127–2140. doi: 10.1109/TAES.2021.3130212.
    [16]
    HUANG Tianyou, LIN Huifu, YANG Chao, et al. A 2-D multiplication modulation jamming method against high-resolution spaceborne SAR based on defocus correction[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4005805. doi: 10.1109/LGRS.2025.3534222.
    [17]
    LIU Yangyang, LAN Lan, ZHONG Lei, et al. A deceptive jamming approach for SAR based on range-azimuth modulation[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4013205. doi: 10.1109/LGRS.2025.3613363.
    [18]
    赵博, 陈基, 黄磊. 单比特多模态SAR干扰方法研究[J]. 雷达学报, 2022, 11(6): 1119–1130. doi: 10.12000/JR22176.

    ZHAO Bo, CHEN Ji, and HUANG Lei. One-bit multi-modality jamming method against SAR[J]. Journal of Radars, 2022, 11(6): 1119–1130. doi: 10.12000/JR22176.
    [19]
    DING Chang, MU Huilin, SHI Yunzhou, et al. Dual-polarized and conformal time-modulated metasurface-based 2-D jamming against SAR imaging systems[J]. IEEE Transactions on Antennas and Propagation, 2025, 73(10): 7752–7764. doi: 10.1109/TAP.2025.3581400.
    [20]
    WANG Bing, CUI Guolong, ZHANG Shuai, et al. Deceptive jamming suppression based on coherent cancelling in multistatic radar system[C]. 2016 IEEE Radar Conference (RadarConf), Philadelphia, USA, 2016: 1–5. doi: 10.1109/RADAR.2016.7485304.
    [21]
    ZHAO Bo, HUANG Lei, LI Jian, et al. Target reconstruction from deceptively jammed single-channel SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(1): 152–167. doi: 10.1109/TGRS.2017.2744178.
    [22]
    SHANG Yuanzhe, PU Wei, WU Congwen, et al. HDSS-Net: A novel hierarchically designed network with spherical space classifier for ship recognition in SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5222420. doi: 10.1109/TGRS.2023.3332137.
    [23]
    王佳祥, 孟进, 李伟, 等. YOLO-S3: 一种轻量化的雷达复合干扰识别网络[J]. 雷达学报(中英文). doi: 10.12000/JR25080.

    WANG Jiaxiang, MENG Jin, LI Wei, et al. YOLO-S3: A lightweight network for radar composite jamming signal recognition[J]. Journal of Radars. doi: 10.12000/JR25080.
    [24]
    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.
    [25]
    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.
    [26]
    SHAO Guangqing, CHEN Yushi, and WEI Yinsheng. Convolutional neural network-based radar jamming signal classification with sufficient and limited samples[J]. IEEE Access, 2020, 8: 80588–80598. doi: 10.1109/ACCESS.2020.2990629.
    [27]
    陈泽伟, 严远鹏. 基于改进DCGAN的毫米波雷达相互干扰时频图像生成研究——以生成样本对CNN干扰抑制模型性能影响为例[J]. 现代信息科技, 2022, 6(13): 55–61. doi: 10.19850/j.cnki.2096-4706.2022.013.014.

    CHEN Zewei and YAN Yuanpeng. Research on generation of MMW radar mutual interference time-frequency image based on improved DCGAN-a case of performance effect of the generated samples on the CNN interference suppression model[J]. Modern Information Technology, 2022, 6(13): 55–61. doi: 10.19850/j.cnki.2096-4706.2022.013.014.
    [28]
    王重淞, 蒲巍, 高杰, 等. 低干信比SAR非虚假目标类有源干扰的轻量化鉴别网络[J]. 雷达学报(中英文). doi: 10.12000/JR25195.

    WANG Chongsong, PU Wei, GAO Jie, et al. Lightweight discrimination network for non-spoofing active jamming in SAR under low JSR[J]. Journal of Radars. doi: 10.12000/JR25195.
    [29]
    刘永才. 对多模式高分辨合成孔径雷达的大场景欺骗干扰技术研究[D]. [博士论文], 国防科技大学, 2017. doi: 10.27052/d.cnki.gzjgu.2017.000037.

    LIU Yongcai. Study on large-area deceptive jamming against multi-mode high-resolution synthetic aperture radar[D]. [Ph.D. dissertation], National University of Defense Technology, 2017. doi: 10.27052/d.cnki.gzjgu.2017.000037.
    [30]
    SONG Yue, PU Wei, ZHANG Yuhua, et al. Analysis of BiSAR images characteristics: From an electromagnetic scattering perspective[J]. IEEE Transactions on Aerospace and Electronic Systems, 2026, 62: 2852–2869. doi: 10.1109/TAES.2025.3641918.
    [31]
    SONG Yue, PU Wei, ZHANG Yin, et al. A structure-driven multistage trajectory planning method for BiSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 2005114. doi: 10.1109/TGRS.2025.3642973.
    [32]
    邢孟道, 谢意远, 高悦欣, 等. 电磁散射特征提取与成像识别算法综述[J]. 雷达学报, 2022, 11(6): 921–942. doi: 10.12000/JR22232.

    XING Mengdao, XIE Yiyuan, GAO Yuexin, et al. Electromagnetic scattering characteristic extraction and imaging recognition algorithm: A review[J]. Journal of Radars, 2022, 11(6): 921–942. doi: 10.12000/JR22232.
    [33]
    赵博. 合成孔径雷达欺骗干扰方法研究[D]. [博士论文], 西安电子科技大学, 2015.

    ZHAO Bo. Study on methods of synthetic aperture radar deception jamming[D]. [Ph.D. dissertation], Xidian University, 2015.
    [34]
    纪朋徽, 邢世其, 代大海, 等. 基于逆chirp scaling的合成孔径雷达卷积欺骗干扰方法[J]. 电波科学学报, 2023, 38(6): 1029–1039. doi: 10.12265/j.cjors.2022246.

    JI Penghui, XING Shiqi, DAI Dahai, et al. A SAR convolutional deceptive jamming method based on the inverse chirp scaling[J]. Chinese Journal of Radio Science, 2023, 38(6): 1029–1039. doi: 10.12265/j.cjors.2022246.
    [35]
    LIU Yongcai, WANG Wei, PAN Xiaoyi, et al. A frequency-domain three-stage algorithm for active deception jamming against synthetic aperture radar[J]. IET Radar, Sonar & Navigation, 2014, 8(6): 639–646. doi: 10.1049/iet-rsn.2013.0222.
    [36]
    ZHANG Tianfang, LI Lei, ZHOU Yang, et al. CAS-ViT: Convolutional additive self-attention vision transformers for efficient mobile applications[J]. IEEE Transactions on Image Processing, 2026, 35: 1899–1909. doi: 10.1109/TIP.2026.3655121.
    [37]
    LIU Xinyu, PENG Houwen, ZHENG Ningxin, et al. EfficientViT: Memory efficient vision transformer with cascaded group attention[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 14420–14430. doi: 10.1109/CVPR52729.2023.01386.
    [38]
    PAN Junting, BULAT A, TAN Fuwen, et al. EdgeViTs: Competing light-weight CNNs on mobile devices with vision transformers[C]. 17th European Conference on Computer Vision, Tel Aviv, Israel, 2022: 294–311. doi: 10.1007/978-3-031-20083-0_18.
    [39]
    GERRY M J, POTTER L C, GUPTA I J, et al. A parametric model for synthetic aperture radar measurements[J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188. doi: 10.1109/8.785750.
    [40]
    段佳. SAR/ISAR目标电磁特征提取及应用研究[D]. [博士论文], 西安电子科技大学, 2015. doi: 10.7666/d.Y2954112.

    DUAN J. Study on electro-magnetic feature extraction of SAR/ISAR and its applications[D]. [Ph.D. dissertation], Xidian University, 2015. doi: 10.7666/d.Y2954112.
    [41]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016: 770–778. doi: 10.1109/CVPR.2016.90.
    [42]
    SZEGEDY C, IOFFE S, VANHOUCKE V, et al. Inception-v4, inception-ResNet and the impact of residual connections on learning[C]. The 31st AAAI Conference on Artificial Intelligence, San Francisco, USA, 2017: 4278–4284. doi: 10.1609/aaai.v31i1.11231.
    [43]
    CHOLLET F. Xception: Deep learning with depthwise separable convolutions[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 1800–1807. doi: 10.1109/CVPR.2017.195.
    [44]
    SU Xiu, HUANG Tao, LI Yanxi, et al. Prioritized architecture sampling with Monto-Carlo tree search[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, 2021: 10963–10972. doi: 10.1109/CVPR46437.2021.01082.
    [45]
    YOU Shan, HUANG Tao, YANG Mingmin, et al. GreedyNAS: Towards fast one-shot NAS with greedy supernet[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA, 2020: 1996–2005. doi: 10.1109/CVPR42600.2020.00207.
    [46]
    CAI Han, WANG Tianzhe, WU Zhanghao, et al. On-device image classification with proxyless neural architecture search and quantization-aware fine-tuning[C]. 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019: 2509–2513. doi: 10.1109/ICCVW.2019.00307.
    [47]
    LIU Hanxiao, SIMONYAN K, and YANG Yiming. DARTS: Differentiable architecture search[C]. 7th International Conference on Learning Representations, New Orleans, USA, 2019: 1–14.
    [48]
    HUANG Tao, LIU Jiaqi, YOU Shan, et al. Active generation for image classification[C]. 18th European Conference on Computer Vision, Milan, Italy, 2024: 270–286. doi: 10.1007/978-3-031-73195-2_16.
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