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ZHOU Zhuojie, LI Yueli, LIU Ke, et al. Multichannel radar forward-looking imaging method based on dual-network cooperation[J]. Journal of Radars, in press. doi: 10.12000/JR25121
Citation: ZHOU Zhuojie, LI Yueli, LIU Ke, et al. Multichannel radar forward-looking imaging method based on dual-network cooperation[J]. Journal of Radars, in press. doi: 10.12000/JR25121
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Multichannel Radar Forward-looking Imaging Method Based on Dual-network Cooperation

DOI: 10.12000/JR25121 CSTR: 32380.14.JR25121

  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Funds:  The National Ministries Foundation
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  • Corresponding author: LI Yueli, liyueli4uwb@nudt.edu.cn
  • Received Date: 2025-07-04
  • Rev Recd Date: 2025-10-26
    • Available Online: 2025-10-29
    Abstract
  • The challenge of distinguishing multiple targets and mitigating image blurry caused by Doppler gradient disappearance in the forward-looking direction of moving platforms is addressed through a multichannel radar forward-looking imaging method based on dual-network collaboration. The proposed method establishes a hierarchical, cascaded, end-to-end processing framework. First, a target Numerical Estimation Network (NEN) predicts the number of targets within the main lobe by analyzing the characteristics of the echo covariance matrix. Then, according to the estimated target count, a pretrained Angle Estimation Network (AEN) model is dynamically selected to determine the azimuth angles of the targets. Finally, target intensity estimation and two-dimensional projection imaging are performed in combination with an improved iterative adaptive algorithm. Simulation and experimental results demonstrate that, compared with conventional super-resolution algorithms, the proposed method achieves more effective simultaneous estimation and accurate reconstruction of parameters for both strong and weak targets in the forward-looking region. Specifically, it attains 86.75% accuracy in target number estimation, while the root mean square error of angle estimation remains below 0.2° in two-target scenarios, significantly enhancing the quality of forward-looking imaging.

     

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    • References(46)
  • [1]
    MAO Deqing, ZHANG Yongchao, PEI Jifang, et al. Forward-looking geometric configuration optimization design for spaceborne-airborne multistatic synthetic aperture radar[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 8033–8047. doi: 10.1109/JSTARS.2021.3103802.
    [2]
    CHEN Hongmeng, LI Yachao, GAO Wenquan, et al. Bayesian forward-looking superresolution imaging using Doppler deconvolution in expanded beam space for high-speed platform[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5105113. doi: 10.1109/TGRS.2021.3107717.
    [3]
    吴迪, 朱岱寅, 朱兆达. 机载雷达单脉冲前视成像算法[J]. 中国图象图形学报, 2010, 15(3): 462–469. doi: 10.11834/jig.20100317.

    WU Di, ZHU Daiyin, and ZHU Zhaoda. Research on nomopulse forward-looking imaging algorithm for airborne radar[J]. Journal of Image and Graphics, 2010, 15(3): 462–469. doi: 10.11834/jig.20100317.
    [4]
    吴迪, 朱岱寅, 田斌, 等. 单脉冲成像算法性能分析[J]. 航空学报, 2012, 33(10): 1905–1914.

    WU Di, ZHU Daiyin, TIAN Bin, et al. Performance evaluation for monopulse imaging algorithm[J]. Acta Aeronautica et Astronautica Sinica, 2012, 33(10): 1905–1914.
    [5]
    LIU Mingjie, WU Di, and REN Lingyun. Monopulse imaging technology based on super-resolution in Doppler domain[C]. The 2nd International Conference on Consumer Electronics and Computer Engineering, Guangzhou, China, 2022: 186–190. doi: 10.1109/ICCECE54139.2022.9712743.
    [6]
    CHENG Cheng, ZHOU Xiaodong, GAO Min, et al. Research on monopulse forward-looking high-resolution imaging algorithm based on adaptive iteration[J]. Defence Technology, 2020, 16(1): 158–171. doi: 10.1016/j.dt.2019.06.008.
    [7]
    李悦丽, 梁甸农, 黄晓涛. 一种单脉冲雷达多通道解卷积前视成像方法[J]. 信号处理, 2007, 23(5): 699–703. doi: 10.3969/j.issn.1003-0530.2007.05.013.

    LI Yueli, LIANG Diannong, and HUANG Xiaotao. A multi-channel deconvolution based on forword-looking imaging method in monopulse radar[J]. Signal Processing, 2007, 23(5): 699–703. doi: 10.3969/j.issn.1003-0530.2007.05.013.
    [8]
    刘可, 李悦丽, 戴永鹏, 等. 基于快速迭代插值多普勒频率估计的单脉冲前视成像技术[J]. 雷达学报, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145.

    LIU Ke, LI Yueli, DAI Yongpeng, et al. Monopulse forward-looking imaging based on Doppler estimation using fast iterative interpolated beamforming algorithm[J]. Journal of Radars, 2023, 12(6): 1138–1154. doi: 10.12000/JR23145.
    [9]
    LI Zhongyu, LI Shanchuan, LIU Zhutian, et al. Bistatic forward-looking SAR MP-DPCA method for space-time extension clutter suppression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(9): 6565–6579. doi: 10.1109/TGRS.2020.2977982.
    [10]
    YANG Jianyu, GU Xueyu, LI Wenchao, et al. Configuration design of bistatic forward-looking SAR driven by spatial resolution metrics[J]. IEEE Geoscience and Remote Sensing Letters, 2025, 22: 4001905. doi: 10.1109/LGRS.2024.3521450.
    [11]
    刘裕洲, 蔡天倚, 李亚超, 等. 联合距离方位二维NCS的星弹双基前视SAR成像算法[J]. 雷达学报, 2023, 12(6): 1202–1214. doi: 10.12000/JR23144.

    LIU Yuzhou, CAI Tianyi, LI Yachao, et al. A range and azimuth combined two-dimensional NCS algorithm for spaceborne-missile bistatic forward-looking SAR[J]. Journal of Radars, 2023, 12(6): 1202–1214. doi: 10.12000/JR23144.
    [12]
    杨泽慧, 聂炜航, 程高峰, 等. 全变分约束的解卷积常规波束形成方位谱估计算法[J]. 声学学报, 2025, 50(1): 68–76. doi: 10.12395/0371-0025.2023173.

    YANG Zehui, NIE Weihang, CHENG Gaofeng, et al. Total variation constrained deconvolved conventional beamforming algorithm for azimuthal spectral estimation[J]. Acta Acustica, 2025, 50(1): 68–76. doi: 10.12395/0371-0025.2023173.
    [13]
    TANG Junkui, RAN Lei, LIU Zheng, et al. A weighted low-rank and sparse constraint-based multichannel radar forward-looking imaging method[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 12973–12987. doi: 10.1109/JSTARS.2025.3568783.
    [14]
    TUO Xingyu, ZHANG Yin, HUANG Yulin, et al. A fast sparse azimuth super-resolution imaging method of real aperture radar based on iterative reweighted least squares with linear sketching[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2928–2941. doi: 10.1109/JSTARS.2021.3061430.
    [15]
    李悦丽, 马萌恩, 赵崇辉, 等. 基于单脉冲雷达和差通道多普勒估计的前视成像[J]. 雷达学报, 2021, 10(1): 131–142. doi: 10.12000/JR20111.

    LI Yueli, MA Meng’en, ZHAO Chonghui, et al. Forward-looking imaging via Doppler estimates of sum-difference measurements in scanning monopulse radar[J]. Journal of Radars, 2021, 10(1): 131–142. doi: 10.12000/JR20111.
    [16]
    DAI Shengli and WIESBECK W. High resolution imaging for forward looking SAR with multiple receiving antennas[C]. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings, Honolulu, USA, 2000: 1433–1435. doi: 10.1109/IGARSS.2000.858373.
    [17]
    DAI Shengli and WIESBECK W. The imaging mode of forward looking SAR with two receiving antennas[C]. 1999 International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 1999: 1776–1778. doi: 10.1109/IGARSS.1999.772092.
    [18]
    王鑫硕, 卢景月, 孟智超, 等. 前视多通道SAR成像及阵列姿态误差补偿[J]. 雷达学报, 2023, 12(6): 1155–1165. doi: 10.12000/JR23073.

    WANG Xinshuo, LU Jingyue, MENG Zhichao, et al. Forward-looking multi-channel synthetic aperture radar imaging and array attitude error compensation[J]. Journal of Radars, 2023, 12(6): 1155–1165. doi: 10.12000/JR23073.
    [19]
    CHEN Rui, LI Wenchao, ZHANG Yongchao, et al. Forward looking imaging of airborne multichannel radar based on modified IAA[C]. 2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022: 2987–2990. doi: 10.1109/IGARSS46834.2022.9884918.
    [20]
    王宁, 贺鹏超, 卢景月, 等. 基于DOA估计的前视多通道SAR成像方法[J]. 系统工程与电子技术, 2023, 45(8): 2471–2478. doi: 10.12305/j.issn.1001-506X.2023.08.21.

    WANG Ning, HE Pengchao, LU Jingyue, et al. DOA estimation based imaging method for multi-channel forward-looking SAR[J]. Systems Engineering and Electronics, 2023, 45(8): 2471–2478. doi: 10.12305/j.issn.1001-506X.2023.08.21.
    [21]
    杨洋, 李悦丽. 单脉冲前视成像多目标分辨算法[J]. 信号处理, 2016, 32(9): 1055–1064. doi: 10.16798/j.issn.1003-0530.2016.09.07.

    YANG Yang and LI Yueli. Multi-targets discrimination algorithm in monopulse forward-looking imaging[J]. Journal of Signal Processing, 2016, 32(9): 1055–1064. doi: 10.16798/j.issn.1003-0530.2016.09.07.
    [22]
    陈洪猛, 鲁耀兵, 刘京, 等. 一种用于月面着陆的知识辅助单脉冲前视成像方法[J]. 载人航天, 2019, 25(1): 31–36. doi: 10.3969/j.issn.1674-5825.2019.01.005.

    CHEN Hongmeng, LU Yaobing, LIU Jing, et al. A knowledge aided monopulse forward-looking imaging algorithm for lunar landing[J]. Manned Spaceflight, 2019, 25(1): 31–36. doi: 10.3969/j.issn.1674-5825.2019.01.005.
    [23]
    陈洪猛, 李明, 王泽玉, 等. 基于多帧数据联合处理的机载单通道雷达贝叶斯前视成像[J]. 电子与信息学报, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153.

    CHEN Hongmeng, LI Ming, WANG Zeyu, et al. Bayesian forward-looking imaging for airborne single-channel radar based on combined multiple frames data[J]. Journal of Electronics & Information Technology, 2015, 37(10): 2328–2334. doi: 10.11999/JEIT150153.
    [24]
    陈洪猛, 余继周, 张文杰, 等. 基于概率模型驱动的机载贝叶斯前视超分辨多目标成像方法[J]. 雷达学报, 2023, 12(6): 1125–1137. doi: 10.12000/JR23080.

    CHEN Hongmeng, YU Jizhou, ZHANG Wenjie, et al. Probability model-driven airborne Bayesian forward-looking super-resolution imaging for multitarget scenario[J]. Journal of Radars, 2023, 12(6): 1125–1137. doi: 10.12000/JR23080.
    [25]
    张永超, 孙震宇, 蔡晓春, 等. 实孔径雷达无超参数全变差正则化角超分辨方法[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR25011.

    ZHANG Yongchao, SUN Zhenyu, CAI Xiaochun, et al. A hyperparameter-free total variation regularization method for real aperture radar angular super-resolution[J]. Journal of Radars, in press. doi: 10.12000/JR25011.
    [26]
    ZHANG Jie, WU Di, ZHU Daiyin, et al. An airborne/missile-borne array radar forward-looking imaging algorithm based on super-resolution method[C]. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Shanghai, China, 2017: 1–5. doi: 10.1109/CISP-BMEI.2017.8302131.
    [27]
    XI Rongyan, ZHENG Chundi, HUANG Tianyao, et al. Joint range and angle estimation for wideband forward-looking imaging radar[J]. IEEE Sensors Journal, 2022, 22(1): 446–460. doi: 10.1109/JSEN.2021.3126206.
    [28]
    ZHOU Zhuojie, LI Yueli, and LIU Ke. A Doppler estimation algorithm for radar imaging based on low-rank embedding theory[C]. 2024 IEEE 17th International Conference on Signal Processing, Suzhou, China, 2024: 821–826. doi: 10.1109/ICSP62129.2024.10846578.
    [29]
    任凌云, 吴迪, 朱岱寅, 等. 基于机载多通道雷达迭代超分辨估计的前视成像[J]. 雷达学报, 2023, 12(6): 1166–1178. doi: 10.12000/JR23085.

    REN Lingyun, WU Di, ZHU Daiyin, et al. Forward-looking imaging via iterative super-resolution estimation in airborne multi-channel radar[J]. Journal of Radars, 2023, 12(6): 1166–1178. doi: 10.12000/JR23085.
    [30]
    LIU Ke, LI Yueli, XU Zhou, et al. Airborne multi-channel forward-looking radar super-resolution imaging using improved fast iterative interpolated beamforming algorithm[J]. Remote Sensing, 2024, 16(22): 4121. doi: 10.3390/rs16224121.
    [31]
    罗迎, 倪嘉成, 张群. 基于“数据驱动+智能学习”的合成孔径雷达学习成像[J]. 雷达学报, 2020, 9(1): 107–122. doi: 10.12000/JR19103.

    LUO Ying, NI Jiacheng, and ZHANG Qun. Synthetic aperture radar learning-imaging method based on data-driven technique and artificial intelligence[J]. Journal of Radars, 2020, 9(1): 107–122. doi: 10.12000/JR19103.
    [32]
    ZHOU Jie, LIU Yongxiang, PENG Bowen, et al. MaDiNet: Mamba diffusion network for SAR target detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2025, early access. doi: 10.1109/TCSVT.2025.3574657.
    [33]
    WU Xiaohuan, YANG Xu, JIA Xiaoyuan, et al. A gridless DOA estimation method based on convolutional neural network with toeplitz prior[J]. IEEE Signal Processing Letters, 2022, 29: 1247–1251. doi: 10.1109/LSP.2022.3176211.
    [34]
    张群, 张宏伟, 倪嘉成, 等. 合成孔径雷达深度学习成像研究综述[J]. 信号处理, 2023, 39(9): 1521–1551. doi: 10.16798/j.issn.1003-0530.2023.09.001.

    ZHANG Qun, ZHANG Hongwei, NI Jiacheng, et al. A survey of synthetic aperture radar imaging methods based on deep learning[J]. Journal of Signal Processing, 2023, 39(9): 1521–1551. doi: 10.16798/j.issn.1003-0530.2023.09.001.
    [35]
    吴明华, 饶彬, 王伟. 基于深度残差网络的雷达目标数量估计方法[J]. 太赫兹科学与电子信息学报, 2022, 20(3): 213–217. doi: 10.11805/TKYDA2021354.

    WU Minghua, RAO Bin, and WANG Wei. Radar target number estimation method based on deep residual network[J]. Journal of Terahertz Science and Electronic Information Technology, 2022, 20(3): 213–217. doi: 10.11805/TKYDA2021354.
    [36]
    孙晓翰, 李凉海, 张彬. 基于CNN-LSTM神经网络的前视成像算法[J]. 遥测遥控, 2024, 45(2): 29–36. doi: 10.12347/j.ycyk.20231225001.

    SUN Xiaohan, LI Lianghai, and ZHANG Bin. Forward-looking imaging algorithm based on CNN-LSTM neural network[J]. Journal of Telemetry, Tracking and Command, 2024, 45(2): 29–36. doi: 10.12347/j.ycyk.20231225001.
    [37]
    ZHANG Ye, YANG Qi, ZENG Yang, et al. High-quality interferometric inverse synthetic aperture radar imaging using deep convolutional networks[J]. Microwave and Optical Technology Letters, 2020, 62(9): 3060–3065. doi: 10.1002/mop.32411.
    [38]
    任凌云. 机载多通道雷达超分辨前视成像技术研究[D]. [硕士论文], 南京航空航天大学, 2023: 26–27. doi: 10.27239/d.cnki.gnhhu.2023.000880.

    REN Lingyun. Research on airborne multi-channel radar super-resolution forward looking imaging[D], [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2023: 26–27. doi: 10.27239/d.cnki.gnhhu.2023.000880.
    [39]
    STOICA P and NEHORAI A. MUSIC, maximum likelihood, and Cramer-Rao bound[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(5): 720–741. doi: 10.1109/29.17564.
    [40]
    ROY III R H and KAILATH T. ESPRIT-estimation osignal parameters via rotational invariance techniques[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1989, 37(7): 984–995. doi: 10.1109/29.32276.
    [41]
    HU Jie, SHEN Li, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011–2023. doi: 10.1109/TPAMI.2019.2913372.
    [42]
    潘耀雄. 基于深度神经网络的机载雷达前视成像技术研究[D]. [硕士论文], 南京航空航天大学, 2023: 120–137. doi: 10.27239/d.cnki.gnhhu.2023.003682.

    PAN Yaoxiong. Research on forward-looking imaging technology of airborne radar based on deep neural network[D]. [Master dissertation], Nanjing University of Aeronautics and Astronautics, 2023: 120–137. doi: 10.27239/d.cnki.gnhhu.2023.003682.
    [43]
    WAX M and KAILATH T. Detection of signals by information theoretic criteria[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 387–392. doi: 10.1109/TASSP.1985.1164557.
    [44]
    KUNDU D and MITRA A. Estimating the number of signals of the damped exponential models[J]. Computational Statistics & Data Analysis, 2001, 36(2): 245–256. doi: 10.1016/S0167-9473(00)00036-0.
    [45]
    KASE Y, NISHIMURA T, OHGANE T, et al. DOA estimation of two targets with deep learning[C]. 2018 15th Workshop on Positioning, Navigation and Communications, Bremen, Germany, 2018: 1–5. doi: 10.1109/WPNC.2018.8555814.
    [46]
    ROBERTS W, STOICA P, LI Jian, et al. Iterative adaptive approaches to MIMO radar imaging[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1): 5–20. doi: 10.1109/JSTSP.2009.2038964.
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