高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

零记忆增量学习的复合有源干扰识别

吴振华 崔金鑫 曹宜策 张强 张磊 杨利霞

吴振华, 崔金鑫, 曹宜策, 张强, 张磊, 杨利霞. 零记忆增量学习的复合有源干扰识别[J]. 电子与信息学报. doi: 10.11999/JEIT240521
引用本文: 吴振华, 崔金鑫, 曹宜策, 张强, 张磊, 杨利霞. 零记忆增量学习的复合有源干扰识别[J]. 电子与信息学报. doi: 10.11999/JEIT240521
WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521
Citation: WU Zhenhua, CUI Jinxin, CAO Yice, ZHANG Qiang, ZHANG Lei, YANG Lixia. Compound Active Jamming Recognition for Zero-memory Incremental Learning[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240521

零记忆增量学习的复合有源干扰识别

doi: 10.11999/JEIT240521
基金项目: 国家自然科学基金(62201007, 62401007),中国博士后科学基金(2020M681992),安徽省自然科学基金(2308085QF199)
详细信息
    作者简介:

    吴振华:男,副教授,研究方向为雷达成像、雷达信号处理、雷达智能干扰对抗

    崔金鑫:男,硕士生,研究方向为雷达干扰识别、雷达信号处理

    曹宜策:女,讲师,研究方向为遥感图像的智能解译、雷达信号处理、计算机视觉

    张强:男,研究员,研究方向为天基信号处理

    张磊:男,教授,研究方向为雷达信号处理

    杨利霞:男,教授,研究方向为电磁散射与逆散射、电波传播及天线理论与设计、计算电磁学

    通讯作者:

    曹宜策 yccao@ahu.edu.cn

  • 中图分类号: TN974

Compound Active Jamming Recognition for Zero-memory Incremental Learning

Funds: The National Natural Science Foundation of China (62201007, 62401007), China Postdoctoral Science Foundation (2020M681992), The Natural Science Foundation of Anhui (2308085QF199)
  • 摘要: 非完备、高动态有源干扰对抗作战环境下,现阶段针对库内多类型单一有源干扰样本所优化训练的静态模型,在面对库外类型多样、参数多变、组合方式多元的复合干扰时,模型无法快速更新且难以应对测试样本数非均衡问题。针对此问题,该文提出一种基于零记忆增量学习的雷达复合有源干扰识别方法。首先,利用元学习训练模式对库内单一干扰进行原型学习,训练出高效的特征提取器,使其具备对库外复合干扰特征有效提取能力。进而,基于超维空间和余弦相似度计算,构建零记忆增量学习网络(ZMILN),将复合干扰原型向量映射到超维空间并存储,从而实现识别模型动态更新。此外,为解决样本数非均衡下复合干扰识别问题,设计直推式信息最大化(TIM)测试模块,通过在互信息损失函数中加入散度约束,对识别模型进一步强化训练以应对非均衡测试样本。实验结果表明,该文所提方法在非均衡测试条件下对4种单一干扰和7种复合干扰进行增量学习后,平均识别准确率达到了93.62%。该方法通过对库内多类型单一干扰知识充分提取,实现对多种组合条件下库外复合干扰的快速动态识别。
  • 图  1  雷达回波及干扰信号的时频谱图

    图  2  零记忆增量学习网络

    图  3  原型学习网络详细结构图

    图  4  原型空间更新示意图

    图  5  不同方法下的干扰识别准确率曲线

    图  6  1-way 5-shot设置下的t-SNE可视化图

    图  7  不同基础设置下的干扰识别性能曲线

    表  1  雷达干扰参数

    信号 参数 数值范围
    LFM 信号宽度
    带宽
    采样频率
    10 μs
    50 μs
    125 MHz
    SMSP 干扰个数 3~7
    SNJ 转发个数
    切片长度
    占空比
    3~5
    10 μs
    0.5~0.8
    DFTJ 假目标个数
    假目标时延
    3~7
    1~10 μs
    MISRJ 转发个数
    切片长度
    占空比
    3~5
    10 μs
    0.5~0.8
    下载: 导出CSV

    表  2  增量干扰数据集配置

    阶段 序号 干扰名称 训练样本个数 单次测试样本个数
    基础 1 SMSP 100 1
    2 SNJ 100 2
    3 DFTJ 100 2
    4 MISRJ 100 8
    增量 5 SNJ+SMSP 5 12
    6 SMSP+MISRJ 5 5
    7 DFTJ+MISRJ 5 9
    8 SMSP+DFTJ 5 5
    9 SNJ+DFTJ 5 12
    10 SNJ+MISRJ 5 7
    11 SNJ+SMSP+DFTJ 5 14
    下载: 导出CSV

    表  3  TIM模块对模型的影响(%)

    测试样本分布TIM基础阶段准确率增量阶段准确率平均准确率性能下降率
    12345678
    均衡100.00100.0097.9597.6096.6195.7294.8894.6297.175.38
    $ \times $100.0097.4292.9491.7090.4289.2187.6185.7291.8714.28
    非均衡100.00100.0098.9597.6096.6194.7293.8893.6296.926.38
    $ \times $99.8595.2293.7187.8386.4484.7272.5180.6687.6119.19
    下载: 导出CSV

    表  4  在1-way 5-shot设置下不同方法的干扰识别结果

    方法 基础阶段准确率(%) 增量阶段准确率(%) 平均准确率
    (%)
    性能下降率
    (%)
    训练平均时间
    (min)
    测试平均时间
    (s)
    1 2 3 4 5 6 7 8
    Ft-CNN[8] 100.00 92.22 83.33 74.16 67.77 61.33 55.55 51.55 73.23 48.45 50.59 15.73
    iCaRL[24] 100.00 91.14 85.72 85.83 83.51 82.66 80.75 78.47 86.01 21.53 185.15 20.79
    TOPIC[16] 99.31 89.77 83.54 84.91 84.67 81.03 78.75 75.98 84.74 23.33 141.96 19.86
    FACT[26] 97.55 97.77 93.80 92.57 91.29 88.74 85.45 83.12 91.28 14.43 98.15 12.02
    CEC[23] 98.44 96.74 93.94 92.70 90.61 88.72 85.88 84.62 91.45 13.82 169.64 16.95
    F2M[25] 100.00 95.71 93.44 87.70 86.41 84.72 82.45 80.74 88.89 19.26 78.96 9.93
    本文(ZMILN) 100.00 100.00 98.95 97.60 96.61 94.72 93.88 93.62 96.92 6.38 62.45 36.95
    下载: 导出CSV
  • [1] 魏毅寅, 杨文华. 海战场典型干扰对抗场景及反舰导弹应对策略研究[J]. 战术导弹技术, 2020(5): 1–8. doi: 10.16358/j.issn.1009-1300.2020.1.538.

    WEI Yiyin and YANG Wenhua. Study on typical jamming scenes in naval battle field and countermeasures of anti-ship missile[J]. Tactical Missile Technology, 2020(5): 1–8. doi: 10.16358/j.issn.1009-1300.2020.1.538.
    [2] ZHANG Xiang, LAN Lan, ZHU Shengqi, et al. Intelligent suppression of interferences based on reinforcement learning[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(2): 1400–1415. doi: 10.1109/TAES.2023.3336643.
    [3] 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.
    [4] 周红平, 王子伟, 郭忠义. 雷达有源干扰识别算法综述[J]. 数据采集与处理, 2022, 37(1): 1–20. doi: 10.16337/j.1004-9037.2022.01.001.

    ZHOU Hongping, WANG Ziwei, and GUO Zhongyi. Overview on recognition algorithms of radar active jamming[J]. Journal of Data Acquisition and Processing, 2022, 37(1): 1–20. doi: 10.16337/j.1004-9037.2022.01.001.
    [5] 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.
    [6] 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.
    [7] 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.
    [8] DU Jinbiao, FAN Weiwei, GONG Chen, et al. Aggregated-attention deformable convolutional network for few-shot SAR jamming recognition[J]. Pattern Recognition, 2024, 146: 109990. doi: 10.1016/j.patcog.2023.109990.
    [9] LUO Zhenyu, CAO Yunhe, YEO T S, et al. Few-shot radar jamming recognition network via time-frequency self-attention and global knowledge distillation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5105612. doi: 10.1109/TGRS.2023.3280322.
    [10] ZHOU Hongping, WANG Lei, GUO Zhongyi. Recognition of radar compound jamming based on convolutional neural network[J]. IEEE Transactions on Aerospace and Electronic Systems, 2023, 59(6): 7380–7394. doi: 10.1109/TAES.2023.3288080.
    [11] LI Boran, ZHANG Lei, DAI Jingwei, et al. FETTrans: Analysis of compound interference identification based on bidirectional dynamic feature adaptation of improved transformer[J]. IEEE Access, 2022, 10: 66321–66331. doi: 10.1109/ACCESS.2022.3182010.
    [12] MENG Yunyun, YU Lei, and WEI Yinsheng. Multi-label radar compound jamming signal recognition using complex-valued CNN with jamming class representation fusion[J]. Remote Sensing, 2023, 15(21): 5180. doi: 10.3390/rs15215180.
    [13] ZHOU Hongping, WANG Lei, MA Minghui, et al. Compound radar jamming recognition based on signal source separation[J]. Signal Processing, 2024, 214: 109246. doi: 10.1016/j.sigpro.2023.109246.
    [14] LV Qinzhe, FAN Hanxin, LIU Junliang, et al. Multilabel deep learning-based lightweight radar compound jamming recognition method[J]. IEEE Transactions on Instrumentation and Measurement, 2024, 73: 2521115. doi: 10.1109/TIM.2024.3400337.
    [15] KONG Yukai, XIA Senlin, DONG Luxin, et al. Compound jamming recognition via contrastive learning for distributed MIMO radars[J]. IEEE Transactions on Vehicular Technology, 2024, 73(6): 7892–7907. doi: 10.1109/TVT.2024.3358996.
    [16] TAO Xiaoyu, HONG Xiaopeng, CHANG Xinyuan, et al. Few-shot class-incremental learning[C]. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2020: 12183–12192. doi: 10.1109/CVPR42600.2020.01220.
    [17] LI Bin, CUI Zongyong, WANG Haohan, et al. SAR incremental automatic target recognition based on mutual information maximization[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 4005305. doi: 10.1109/LGRS. 2024.3368063.
    [18] SERBES A. On the estimation of LFM signal parameters: Analytical formulation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2018, 54(2): 848–860. doi: 10.1109/TAES.2017.2767978.
    [19] WANG J X. Meta-learning in natural and artificial intelligence[J]. Current Opinion in Behavioral Sciences, 2021, 38: 90–95. doi: 10.1016/j.cobeha.2021.01.002.
    [20] SANTORO A, BARTUNOV S, BOTVINICK M, et al. Meta-learning with memory-augmented neural networks[C]. The 33rd International Conference on International Conference on Machine Learning, New York, USA, 2016: 1842-1850.
    [21] KARUNARATNE G, SCHMUCK M, LE GALLO M, et al. Robust high-dimensional memory-augmented neural networks[J]. Nature Communications, 2021, 12(1): 2468. doi: 10.1038/s41467-021-22364-0.
    [22] VEILLEUX O, BOUDIAF M, PIANTANIDA P, et al. Realistic evaluation of transductive few-shot learning[C]. Proceedings of the 35th International Conference on Neural Information Processing Systems, 2021: 711.
    [23] ZHANG Chi, SONG Nan, LIN Guosheng, et al. Few-shot incremental learning with continually evolved classifiers[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, USA, 2021: 12455–12464. doi: 10.1109/CVPR46437.2021.01227.
    [24] REBUFFI S A, KOLESNIKOV A, SPERL G, et al. iCaRL: Incremental classifier and representation learning[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, USA, 2017: 2001–2010. doi: 10.1109/cvpr.2017.587.
    [25] SHI Guangyuan, CHEN Jiaxin, ZHANG Wenlong, et al. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima[C]. The 35th International Conference on Neural Information Processing Systems, 2021: 517.
    [26] ZHOU Dawei, WANG Fuyun, YE Hanjia, et al. Forward compatible few-shot class-incremental learning[C]. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, USA, 2022: 9046–9056. doi: 10.1109/cvpr52688.2022.00884.
  • 加载中
图(7) / 表(4)
计量
  • 文章访问数:  78
  • HTML全文浏览量:  26
  • PDF下载量:  14
  • 被引次数: 0
出版历程
  • 收稿日期:  2024-06-25
  • 修回日期:  2024-11-07
  • 网络出版日期:  2024-11-13

目录

    /

    返回文章
    返回