Volume 12 Issue 4
Aug.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2023  >  12(4): 882-891
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
Citation: 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
shu

Active Deception Jamming Recognition Method in Multimodal Radar Based on Small Samples

doi: 10.12000/JR23104
  • 1.

    Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China

  • 2.

    School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100190, China

  • 3.

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

Funds:  The National Ministries Foundation
More Information
  • Corresponding author: ZHANG Shunsheng, zhangss@uestc.edu.cn
  • Received Date: 2023-06-11
  • Rev Recd Date: 2023-07-22
    • Available Online: 2023-07-25
  • Publish Date: 2023-08-15
    Abstract
  • Jamming recognition is a prerequisite for radar antijamming and actual radar deception jamming recognition; however, there is a problem of insufficient samples. To address this issue, we propose a multimodal radar active deception jamming recognition method based on small samples in this paper. This method is based on two modal information—feature parameters and time-frequency images extracted from radar signals—and utilizes prototype networks to train multimodal features. Furthermore, the model adopts the image denoising method and weighted Euclidean distance to improve the recognition performance at low signal-to-noise ratios. Thus, radar deception jamming recognition can be achieved under small sample conditions. Simulation results reveal that the proposed method achieves an average recognition accuracy of over 97% across 10 types of radar deception jamming when the jamming-to-signal ratio is 3 dB. Moreover, the test results from the simulator data verify the good generalization performance of the proposed method.

     

    • FullText(HTML)
  • loading
    • References(26)
  • [1]
    KWAK C M. Application of DRFM in ECM for pulse type radar[C]. 2009 34th International Conference on Infrared, Millimeter, and Terahertz Waves, Busan, Korea (South), 2009: 1–2.
    [2]
    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
    [3]
    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
    [4]
    MENDOZA A, SOTO A, and FLORES B C. Classification of radar jammer FM signals using a neural network[C]. SPIE 10188, Radar Sensor Technology XXI, Anaheim, USA, 2017: 101881G.
    [5]
    WU Zhilu, ZHAO Yanlong, YIN Zhengdong, et al. Jamming signals classification using convolutional neural network[C]. 2017 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, 2017: 62–67.
    [6]
    ZHAO Qingyuan, LIU Yang, CAI Linjie, et al. Research on electronic jamming identification based on CNN[C]. 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP), Chongqing, China, 2019: 1–5.
    [7]
    LIU Qiang and ZHANG Wei. Deep learning and recognition of radar jamming based on CNN[C]. 2019 12th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 2019: 208–212.
    [8]
    HOWARD J and RUDER S. Universal language model fine-tuning for text classification[C]. The 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018: 328–339.
    [9]
    DING Kaize, WANG Jianling, LI Jundong, et al. Graph prototypical networks for few-shot learning on attributed networks[C]. The 29th ACM International Conference on Information & Knowledge Management, Online, 2020: 295–304.
    [10]
    CHOPRA S, HADSELL R, and LECUN Y. Learning a similarity metric discriminatively, with application to face verification[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), San Diego, USA, 2005: 539–546.
    [11]
    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
    [12]
    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
    [13]
    KOCH G, ZEMEL R, and SALAKHUTDINOV R. Siamese neural networks for one-shot image recognition[C]. The 32nd International Conference on Machine Learning, Lille, France, 2015: 1–8.
    [14]
    SNELL J, SWERSKY K, and ZEMEL R S. Prototypical networks for few-shot learning[C]. 31st Conference on Neural Information Processing Systems, Long Beach, USA, 2017: 4080–4090.
    [15]
    LU Yunlong and LI Siyu. CFAR detection of DRFM deception jamming based on singular spectrum analysis[C]. 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), Xiamen, China, 2017: 1–6.
    [16]
    定少浒, 汤建龙. 基于SSA的DRFM速度欺骗干扰识别[J]. 雷达科学与技术, 2020, 18(1): 44–50. doi: 10.3969/j.issn.1672-2337.2020.01.008

    DING Shaohu and TANG Jianlong. DRFM velocity deception jamming recognition based on singular spectrum analysis[J]. Radar Science and Technology, 2020, 18(1): 44–50. doi: 10.3969/j.issn.1672-2337.2020.01.008
    [17]
    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, 2021, 60: 5107511. doi: 10.1109/TGRS.2021.3129645
    [18]
    JAMIL N, SEMBOK T M T, and BAKAR Z. A. Noise removal and enhancement of binary images using morphological operations[C]. 2008 International Symposium on Information Technology, Kuala Lumpur, Malaysia, 2008: 1–6.
    [19]
    XI Bobo, LI Jiaojiao, LI Yunsong, et al. Deep prototypical networks with hybrid residual attention for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3683–3700. doi: 10.1109/JSTARS.2020.3004973
    [20]
    利强, 张伟, 金秋园, 等. 基于知识原型网络的小样本多功能雷达工作模式识别[J]. 电子学报, 2022, 50(6): 1344–1350. doi: 10.12263/DZXB.20210932

    LI Qiang, ZHANG Wei, JIN Qiuyuan, et al. Multi-function radar working mode recognition with few samples based on knowledge embedded prototype network[J]. Acta Electronica Sinica, 2022, 50(6): 1344–1350. doi: 10.12263/DZXB.20210932
    [21]
    GAO Meng, LI Hongtao, JIAO Bixuan, et al. Simulation research on classification and identification of typical active jamming against LFM radar[C]. Proceedings of SPIE 11384 Eleventh International Conference on Signal Processing Systems, Nanjing, China, 2019: 113840T.
    [22]
    LAKE B M, SALAKHUTDINOV R, and TENENBAUM J B. The Omniglot challenge: A 3-year progress report[J]. Current Opinion in Behavioral Sciences, 2019, 29: 97–104. doi: 10.1016/j.cobeha.2019.04.007
    [23]
    WANG Jingyi, DONG Wenhao, and SONG Zhiyong. Radar active jamming recognition based on time-frequency image classification[C]. 2021 5th International Conference on Electronic Information Technology and Computer Engineering, Xiamen, China, 2021: 449–454.
    [24]
    TIAN Xinyi, CHEN Baixiao, and ZHANG Zhaoming. Multiresolution jamming recognition with few-shot learning[C]. 2021 CIE International Conference on Radar, Haikou, China, 2021: 2267–2271.
    [25]
    梁先明. 一种优化孪生网络的小样本辐射源个体识别方法[J]. 电讯技术, 2022, 62(6): 695–701. doi: 10.3969/j.issn.1001-893x.2022.06.001

    LIANG Xianming. An emitter individual identification method for small samples based on optimized siamese networks[J]. Telecommunication Engineering, 2022, 62(6): 695–701. doi: 10.3969/j.issn.1001-893x.2022.06.001
    [26]
    GU Jiuxiang, WANG Zhenhua, KUEN J, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77: 354–377. doi: 10.1016/j.patcog.2017.10.013
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(702) PDF downloads(172) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return