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基于门控机制的并行CNN-Transformer神经网络的多参数欺骗干扰检测方法

庄学彬 牛犇 林子健 张林杰

庄学彬, 牛犇, 林子健, 张林杰. 基于门控机制的并行CNN-Transformer神经网络的多参数欺骗干扰检测方法[J]. 电子与信息学报, 2025, 47(6): 2005-2014. doi: 10.11999/JEIT240977
引用本文: 庄学彬, 牛犇, 林子健, 张林杰. 基于门控机制的并行CNN-Transformer神经网络的多参数欺骗干扰检测方法[J]. 电子与信息学报, 2025, 47(6): 2005-2014. doi: 10.11999/JEIT240977
ZHUANG Xuebin, NIU Ben, LIN Zijian, ZHANG Linjie. A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism[J]. Journal of Electronics & Information Technology, 2025, 47(6): 2005-2014. doi: 10.11999/JEIT240977
Citation: ZHUANG Xuebin, NIU Ben, LIN Zijian, ZHANG Linjie. A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism[J]. Journal of Electronics & Information Technology, 2025, 47(6): 2005-2014. doi: 10.11999/JEIT240977

基于门控机制的并行CNN-Transformer神经网络的多参数欺骗干扰检测方法

doi: 10.11999/JEIT240977 cstr: 32379.14.JEIT240977
基金项目: 航空科学基金(202300080M1001)
详细信息
    作者简介:

    庄学彬:男,教授,研究方向为智能导航对抗

    牛犇:男,硕士生,研究方向为导航欺骗信号检测

    林子健:男,硕士生,研究方向为导航干扰信号检测

    张林杰:男,博士生,研究方向为导航干扰信号检测

    通讯作者:

    庄学彬 zhuangxb@mail.sysu.edu.cn

  • 中图分类号: TN97

A Multiparameter Spoofing Detection Method Based on Parallel CNN-Transformer Neural Network with Gating Mechanism

Funds: The Aeronautical Science Foundation (202300080M1001)
  • 摘要: 在日益复杂的电磁环境中,卫星导航信号极易受到欺骗干扰,因此有必要对欺骗干扰进行检测。针对多种欺骗干扰场景下,单一参数欺骗检测方法不通用、不够鲁棒的问题,该文提出了一种基于门控机制的并行CNN-Transformer神经网络(PCTN)的多参数欺骗干扰信号检测算法,用于检测接收机是否受到欺骗信号的干扰。该算法考虑了欺骗干扰在跟踪阶段对接收机各参数的影响,并从中提取出多个特征信息组成多维时间序列。对序列进行预处理后,利用PCTN网络提取序列的时间前后联系和多个维度上的特征信息。实验结果表明,所提算法在TEXBAT数据集以及实采数据集上分别取得了99%和94.5%以上的总体检测率,能够有效提升多场景下欺骗干扰检测算法的通用性和泛化性。
  • 图  1  各特征参数的对比曲线图

    图  2  Transformer 模型的编码器结构

    图  3  SENet 的结构示意图

    图  4  PCTN 网络结构

    图  5  基于深度学习的多参数欺骗干扰检测算法的流程图

    图  6  6种不同欺骗干扰检测算法的混淆矩阵热图

    图  7  6 种算法对不同卫星的欺骗干扰检测准确率

    图  8  不同欺骗场景下各模型的 ROC 曲线图

    图  9  欺骗干扰采集平台的整体架构

    图  10  6种再训练模型基于实采数据集的 ROC 曲线图

    表  1  9个特征参数的总结

    符号 特征参数 单位(未滑窗处理前) 观测量
    f1 码率-MV MHz 码率
    f2 载波相位差-MV cycle 鉴别器结果
    f3 载波频率差-MV Hz
    f4 码相位差-MV chip
    f5 多普勒频移-MV Hz 多普勒频移
    f6 载噪比-MA dB 载噪比
    f7 载噪比-MV dB
    f8 Ratio-MA \ SQM指标
    f9 Ratio-MV \
    下载: 导出CSV

    表  2  模型主要参数表

    参数名称 参数值
    特征向量维度(d_model) 512
    前馈网络隐藏层维度(d_hidden) 1 024
    编码器层数 8
    CNN输入通道数 9
    CNN 1维卷积和的大小 4
    批量大小(batch_size) 32
    下载: 导出CSV

    表  3  6种算法在不同欺骗场景下的各项评估指标结果(%)

    算法 评估指标 欺骗场景
    ds2 ds3 ds4 ds8
    SVM 精确度 90.343 94.481 94.157 92.343
    召回率 90.333 94.111 93.833 92.222
    F1得分 90.333 94.099 93.822 92.217
    准确率 90.333 94.111 93.833 92.222
    Bagging 精确度 89.240 91.835 91.167 90.656
    召回率 88.444 90.444 89.944 89.556
    F1得分 88.387 90.365 89.870 89.485
    准确率 88.444 90.444 89.944 89.556
    MLP 精确度 92.890 96.278 95.970 93.559
    召回率 92.889 96.111 95.833 93.556
    F1得分 92.889 96.108 95.830 93.555
    准确率 92.889 96.111 95.833 93.556
    Transformer 精确度 98.165 98.283 98.544 95.732
    召回率 98.111 98.222 98.5 95.722
    F1得分 98.111 98.222 98.5 95.722
    准确率 98.111 98.222 98.5 95.722
    PCLN 精确度 99.001 98.889 99.223 96.192
    召回率 99.000 98.889 99.222 96.056
    F1得分 99.000 98.889 99.222 96.053
    准确率 99.000 98.889 99.222 96.056
    PCTN 精确度 99.779 99.667 99.668 97.214
    召回率 99.778 99.667 99.667 97.111
    F1得分 99.778 99.667 99.667 97.110
    准确率 99.778 99.667 99.667 97.111
    下载: 导出CSV

    表  4  6种算法在实采数据集上的各项评估指标结果(%)

    评估指标 SVM Bagging MLP Transformer PCLN PCTN
    精确度 82.248 76.249 72.404 80.852 87.66 94.579
    召回率 81.439 73.404 71.509 80.649 87.491 94.526
    F1得分 81.325 72.676 71.231 80.617 87.477 94.525
    准确率 81.439 73.404 71.509 80.649 87.491 94.526
    下载: 导出CSV
  • [1] HEIN G W. Status, perspectives and trends of satellite navigation[J]. Satellite Navigation, 2020, 1(1): 22. doi: 10.1186/s43020-020-00023-x.
    [2] 王芝应, 聂俊伟, 李峥嵘, 等. BOC接收机捕获阶段转发式欺骗信号检测算法[J]. 全球定位系统, 2016, 41(5): 13–17. doi: 10.13442/j.gnss.1008-9268.2016.05.003.

    WANG Zhiying, NIE Junwei, LI Zhengrong, et al. A repeater spoofing signal detection algorithm in GNSS acquisition period of BOC modulation receiver[J]. GNSS World of China, 2016, 41(5): 13–17. doi: 10.13442/j.gnss.1008-9268.2016.05.003.
    [3] 王文益, 王沛菡. 基于捕获结果的GNSS欺骗式干扰检测[J]. 信号处理, 2021, 37(8): 1460–1469. doi: 10.16798/j.issn.1003-0530.2021.08.013.

    WANG Wenyi and WANG Peihan. GNSS spoofing interference detection based on acquisition results[J]. Journal of Signal Processing, 2021, 37(8): 1460–1469. doi: 10.16798/j.issn.1003-0530.2021.08.013.
    [4] SHANG Shunshun, LI Hong, WEI Yimin, et al. GNSS spoofing detection and identification based on clock drift monitoring using only one signal[C]. 2020 International Technical Meeting of the Institute of Navigation, San Diego, USA, 2020: 331–340. doi: 10.33012/2020.17147.
    [5] YUAN Dingbo, LI Hong, WANG Fei, et al. A GNSS acquisition method with the capability of spoofing detection and mitigation[J]. Chinese Journal of Electronics, 2018, 27(1): 213–222. doi: 10.1049/cje.2017.11.001.
    [6] FANG Jingxiaotao, YUE Jiang, XU Bing, et al. A post-correlation graphical way for continuous GNSS spoofing detection[J]. Measurement, 2023, 216: 112974. doi: 10.1016/j.measurement.2023.112974.
    [7] 王璐, 张林杰, 吴仁彪. 功率监测与SQM融合的GNSS欺骗干扰检测[J]. 信号处理, 2023, 39(3): 505–515. doi: 10.16798/j.issn.1003-0530.2023.03.013.

    WANG Lu, ZHANG Linjie, and WU Renbiao. GNSS spoofing detection based on power monitoring combined with SQM[J]. Journal of Signal Processing, 2023, 39(3): 505–515. doi: 10.16798/j.issn.1003-0530.2023.03.013.
    [8] 许睿, 岳帅, 唐瑞琪, 等. 欺骗环境下GNSS信号估计与定位修正技术[J]. 航空学报, 2020, 41(10): 323930. doi: 10.7527/S1000-6893.2020.23930.

    XU Rui, YUE Shuai, TANG Ruiqi, et al. GNSS signal estimation and position correction algorithm under spoofing attacks[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(10): 323930. doi: 10.7527/S1000-6893.2020.23930.
    [9] 贾琼琼, 朱传胜. 基于多通道SQM指标联合的矢量接收机多径干扰检测方法[J]. 全球定位系统, 2023, 48(3): 110–119. doi: 10.12265/j.gnss.2023037.

    JIA Qiongqiong and ZHU Chuansheng. Multipath interference detection method for vector receivers based on joint multi-channel SQM metrics[J]. GNSS World of China, 2023, 48(3): 110–119. doi: 10.12265/j.gnss.2023037.
    [10] ROTHMAIER F, CHEN Y H, LO S, et al. A framework for GNSS spoofing detection through combinations of metrics[J]. IEEE Transactions on Aerospace and Electronic Systems, 2021, 57(6): 3633–3647. doi: 10.1109/TAES.2021.3082673.
    [11] LI Jing, CHEN Zhengkun, RAN Zixuan, et al. The GNSS spoofing detection method based on AdaBoost[C]. The 2023 6th International Symposium on Autonomous Systems, Nanjing, China, 2023: 1–6. doi: 10.1109/ISAS59543.2023.10164411.
    [12] REN Yingying, RESTIVO R D, TAN Wenkai, et al. Knowledge distillation-based GPS spoofing detection for small UAV[J]. Future Internet, 2023, 15(12): 389. doi: 10.3390/fi15120389.
    [13] LI Lixuan, SUN Chao, ZHAO Hongbo, et al. GNSS spoofing detection using moving variance of signal quality monitoring metrics and signal power[C]. The 14th EAI International Conference on Communications and Networking in China, Shanghai, China, 2020: 537–548. doi: 10.1007/978-3-030-41114-5_40.
    [14] HU Jie, LI Shen, and SUN Gang. Squeeze-and-excitation networks[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7132–7141. doi: 10.1109/CVPR.2018.00745.
    [15] 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.
    [16] LEMMENES A, CORBELL P, and GUNAWARDENA S. Detailed analysis of the TEXBAT datasets using a high fidelity software GPS receiver[C]. The 29th International Technical Meeting of the Satellite Division of the Institute of Navigation, Portland, USA, 2016: 3027–3032. doi: 10.33012/2016.14668.
    [17] HUMPHREYS T E, BHATTI J A, SHEPARD D, et al. The Texas spoofing test battery: Toward a standard for evaluating GPS signal authentication techniques[R]. Austin: The University of Texas at Austin, 2012.
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出版历程
  • 收稿日期:  2024-10-31
  • 修回日期:  2025-03-13
  • 网络出版日期:  2025-03-25
  • 刊出日期:  2025-06-30

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