Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2025  > Online First
WANG Zhaolong, ZHANG Xiaokuan, FENG Weike, et al. Moving target detection method based on intelligent multiclassification and transfer learning for missile-borne radars with sum-difference beams[J]. Journal of Radars, in press. doi: 10.12000/JR25089
Citation: WANG Zhaolong, ZHANG Xiaokuan, FENG Weike, et al. Moving target detection method based on intelligent multiclassification and transfer learning for missile-borne radars with sum-difference beams[J]. Journal of Radars, in press. doi: 10.12000/JR25089
shu

Moving Target Detection Method Based on Intelligent Multiclassification and Transfer Learning for Missile-borne Radars with Sum-difference Beams

DOI: 10.12000/JR25089 CSTR: 32380.14.JR25089

  • Air Defense and Antimissile School, Air Force Engineering University, Xi’an 710051, China

Funds:  Innovation Capability Support Program of Shaanxi (2025ZC-KJXX-81), Research Program Project of Youth Innovation Team of Shaanxi Provincial Education Department (24JP221), The Youth Innovation Team of Shaanxi Universities, National Natural Science Foundation of China (62201611, 62301597, 62301598)
More Information
  • Corresponding author: FENG Weike, fengweike007@163.com
  • Received Date: 2025-05-12
  • Rev Recd Date: 2025-07-31
    • Available Online: 2025-08-04
    Abstract
  • Existing moving-target detection methods for missile-borne sum-difference beam radars require large amounts of training range cell data, yet still exhibit low detection performance. To address these challenges, this paper proposes a new detection method based on intelligent multiclassification and network parameter transfer learning. The proposed method uses a small set of training range cell data to construct a dataset for training a deep Convolutional Neural Network (CNN), which classifies data from the Range Cell Under Test (RCUT) into clutter (target-free) or target classes with different Doppler frequencies. To avoid the high computational cost and time associated with online training on measured data, an echo signal model is first established for moving target detection in the missile-borne sum-difference beam radar. This model is validated using measured data and subsequently used to generate simulated data for offline network training. In addition, to overcome common limitations of typical CNNs, such as large parameter sets, high computing complexity, and low training efficiency, this paper enhances the DenseNet architecture by incorporating a Feature Fusion Module (FFM) and a Spatial Attention Module (SAM), resulting in an improved FFM-SAM-DenseNet multiclassifier. Furthermore, conventional detection methods based on intelligent multiclassification require retraining the network when processing data from different RCUTs, leading to long convergence time and reduced efficiency. To solve this problem, transfer learning is introduced to share network parameters across multiclassifiers for different RCUTs, accelerating the overall convergence speed. Simulation and measured data show that, even with limited training range cell data, the proposed method achieves better moving target detection performance than existing typical methods.

     

    • FullText(HTML)
  • loading
    • References(31)
  • [1]
    HUANG Penghui, YANG Hao, ZOU Zihao, et al. Multichannel clutter modeling, analysis, and suppression for missile-borne radar systems[J]. IEEE Transactions on Aerospace and Electronic Systems, 2022, 58(4): 3236–3260. doi: 10.1109/TAES.2022.3147136.
    [2]
    许京伟, 廖桂生, 朱圣棋, 等. 前视阵高速雷达空时处理方法研究[J]. 电子与信息学报, 2013, 35(3): 509–515. doi: 10.3724/SP.J.1146.2012.00992.

    XU Jingwei, LIAO Guisheng, ZHU Shengqi, et al. Research on STAP approach of forward looking array radar with high-velocity[J]. Journal of Electronics & Information Technology, 2013, 35(3): 509–515. doi: 10.3724/SP.J.1146.2012.00992.
    [3]
    MELVIN W L. A STAP overview[J]. IEEE Aerospace and Electronic Systems Magazine, 2004, 19(1): 19–35. doi: 10.1109/MAES.2004.1263229.
    [4]
    REED I S, MALLETT J D, and BRENNAN L E. Rapid convergence rate in adaptive arrays[J]. IEEE Transactions on Aerospace and Electronic Systems, 1974, AES-10(6): 853–863. doi: 10.1109/TAES.1974.307893.
    [5]
    SHI Jieming, CHENG Ziyang, LI Jun, et al. Pulse interval optimization for Doppler ambiguity clutter suppression in missile-borne STAP radar[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3506205. doi: 10.1109/LGRS.2024.3371438.
    [6]
    何松华, 胡霞, 张军. 弹载宽带相控阵雷达高度目标检测的通道级STAP方法[J]. 信号处理, 2016, 32(5): 528–535. doi: 10.16798/j.issn.1003-0530.2016.05.004.

    HE Songhua, HU Xia, and ZHANG Jun. Channel-level STAP method for lofty target detection of missile-borne wideband phased-array radar[J]. Journal of Signal Processing, 2016, 32(5): 528–535. doi: 10.16798/j.issn.1003-0530.2016.05.004.
    [7]
    XIONG Yuanyi, XIE Wenchong, WANG Yongliang, et al. Short-range nonstationary clutter suppression for airborne KA-STAP radar in complex terrain environment[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 2766–2776. doi: 10.1109/JSTARS.2024.3522257.
    [8]
    LI Jun’ao, LI Zhongyu, YANG Qing, et al. Efficient matrix sparse recovery STAP method based on Kronecker transform for BiSAR sea clutter suppression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5103218. doi: 10.1109/TGRS.2024.3362844.
    [9]
    WANG Xiangrong, ABOUTANIOS E, and AMIN M G. Reduced-rank STAP for slow-moving target detection by antenna-pulse selection[J]. IEEE Signal Processing Letters, 2015, 22(8): 1156–1160. doi: 10.1109/LSP.2015.2390148.
    [10]
    WANG Haihong, DUAN Keqing, SHEN Wei, et al. Reduced-dimensional STAP method for nonstationary clutter suppression in endfire array airborne radar[J]. IEEE Sensors Journal, 2024, 24(17): 27750–27762. doi: 10.1109/JSEN.2024.3429173.
    [11]
    许京伟, 廖桂生, 朱圣棋, 等. 弹载俯冲非正侧阵雷达杂波特性与抑制方法[J]. 系统工程与电子技术, 2013, 35(8): 1631–1637. doi: 10.3969/j.issn.1001-506X.2013.08.08.

    XU Jingwei, LIAO Guisheng, ZHU Shengqi, et al. Clutter characteristic and suppression approach for missile-borne diving non-broadside array radar[J]. Systems Engineering and Electronics, 2013, 35(8): 1631–1637. doi: 10.3969/j.issn.1001-506X.2013.08.08.
    [12]
    李浩冬, 廖桂生, 许京伟. 弹载雷达和差通道稳健自适应杂波抑制方法[J]. 系统工程与电子技术, 2019, 41(2): 273–279. doi: 10.3969/j.issn.1001-506X.2019.02.08.

    LI Haodong, LIAO Guisheng, and XU Jingwei. Robust adaptive clutter suppression approach for missile-borne radar with $\Sigma\Delta $-beam[J]. Systems Engineering and Electronics, 2019, 41(2): 273–279. doi: 10.3969/j.issn.1001-506X.2019.02.08.
    [13]
    EL KHATIB A, ASSALEH K, and MIR H. Learning-based space-time adaptive processing[C]. The 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, United Arab Emirates, 2013: 1–4. doi: 10.1109/ICCSPA.2013.6487279.
    [14]
    EL KHATIB A, ASSALEH K, and MIR H. Space-time adaptive processing using pattern classification[J]. IEEE Transactions on Signal Processing, 2015, 63(3): 766–779. doi: 10.1109/TSP.2014.2385653.
    [15]
    刘喆, 许晓晴. 基于支持向量机的动目标显示方法研究[C]. 第十四届全国雷达学术年会, 成都, 2017: 1–6.

    LIU Zhe and XU Xiaoqing. Research on moving target indication method based on support vector machine[C]. The 14th National Radar Conference, Chengdu, China, 2017: 1–6.
    [16]
    LIU Zhe, HO D K C, XU Xiaoqing, et al. Moving target indication using deep convolutional neural network[J]. IEEE Access, 2018, 6: 65651–65660. doi: 10.1109/ACCESS.2018.2877018.
    [17]
    李贵锋, 童宁宁, 冯为可, 等. 基于DenseNet的机载雷达动目标检测[J]. 空军工程大学学报: 自然科学版, 2021, 22(2): 83–90. doi: 10.3969/j.issn.1009-3516.2021.02.013.

    LI Guifeng, TONG Ningning, FENG Weike, et al. Airborne radar moving target detection based on DenseNet[J]. Journal of Air Force Engineering University: Natural Science Edition, 2021, 22(2): 83–90. doi: 10.3969/j.issn.1009-3516.2021.02.013.
    [18]
    HOU Yunfei, ZHANG Yingnan, GUI Wenzhu, et al. CNN-based moving target detection for airborne radar with controllable false alarm module[J]. IEEE Geoscience and Remote Sensing Letters, 2024, 21: 3508405. doi: 10.1109/LGRS.2024.3438948.
    [19]
    陶海红, 郭晓双, 孙晨伟, 等. 相控阵雷达降维四通道和差波束测角[J]. 北京理工大学学报, 2019, 39(1): 75–80. doi: 10.15918/j.tbit1001-0645.2019.01.013.

    TAO Haihong, GUO Xiaoshuang, SUN Chenwei, et al. Dimensionality reduction of phased array and 4-channel monopulse angle measurement of sum and difference beams[J]. Transactions of Beijing Institute of Technology, 2019, 39(1): 75–80. doi: 10.15918/j.tbit1001-0645.2019.01.013.
    [20]
    佘胜团. 毫米波单脉冲天线阵和低副瓣天线阵的研究[D]. [硕士论文], 电子科技大学, 2006: 30–34.

    SHE Shengtuan. Study of millimetre-wave monopulse antenna arrays and low paraflap antenna arrays[D]. [Master dissertation], University of Electronic Science and Technology of China, 2006: 30–34.
    [21]
    BROWN R D, SCHNEIBLE R A, WICKS M C, et al. STAP for clutter suppression with sum and difference beams[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(2): 634–646. doi: 10.1109/7.845254.
    [22]
    LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278–2324. doi: 10.1109/5.726791.
    [23]
    KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84–90. doi: 10.1145/3065386.
    [24]
    SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[C]. The 3rd International Conference on Learning Representations, San Diego, USA, 2015: 40–49.
    [25]
    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.
    [26]
    HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, 2017: 2261–2269. doi: 10.1109/CVPR.2017.243.
    [27]
    ZHU Xizhou, CHENG Dazhi, ZHANG Zheng, et al. An empirical study of spatial attention mechanisms in deep networks[C]. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019: 6687–6696. doi: 10.1109/ICCV.2019.00679.
    [28]
    SHAO Ling, ZHU Fan, and LI Xuelong. Transfer learning for visual categorization: A survey[J]. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(5): 1019–1034. doi: 10.1109/TNNLS.2014.2330900.
    [29]
    陈一凡, 刘剑刚, 贾勇, 等. 基于仿真样本迁移学习的穿墙雷达高分辨成像方法[J]. 雷达学报(中英文), 2024, 13(4): 807–821. doi: 10.12000/JR24049.

    CHEN Yifan, LIU Jiangang, JIA Yong, et al. High-resolution imaging method for through-the-wall radar based on transfer learning with simulation samples[J]. Journal of Radars, 2024, 13(4): 807–821. doi: 10.12000/JR24049.
    [30]
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification[C]. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 2015: 1026–1034. doi: 10.1109/ICCV.2015.123.
    [31]
    WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment: From error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600–612. doi: 10.1109/TIP.2003.819861.
    • 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(137) PDF downloads(33) Cited by()
    Proportional views
    Related

    京ICP备20021838号-14   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return