宽带逆合成孔径雷达高分辨成像技术综述

田彪 刘洋 呼鹏江 吴文振 徐世友 陈曾平

田彪, 刘洋, 呼鹏江, 等. 宽带逆合成孔径雷达高分辨成像技术综述[J]. 雷达学报, 2020, 9(5): 765–802. doi: 10.12000/JR20060
引用本文: 田彪, 刘洋, 呼鹏江, 等. 宽带逆合成孔径雷达高分辨成像技术综述[J]. 雷达学报, 2020, 9(5): 765–802. doi: 10.12000/JR20060
TIAN Biao, LIU Yang, HU Pengjiang, et al. Review of high-resolution imaging techniques of wideband inverse synthetic aperture radar[J]. Journal of Radars, 2020, 9(5): 765–802. doi: 10.12000/JR20060
Citation: TIAN Biao, LIU Yang, HU Pengjiang, et al. Review of high-resolution imaging techniques of wideband inverse synthetic aperture radar[J]. Journal of Radars, 2020, 9(5): 765–802. doi: 10.12000/JR20060

宽带逆合成孔径雷达高分辨成像技术综述

doi: 10.12000/JR20060
基金项目: 国家自然科学基金(61901481, 61921001),湖南省自然科学基金(2019JJ50715),安徽省自然科学基金(2008085QF283),博士后科学基金(2019TQ0074),国防科技大学科研项目(ZK18-03-58)
详细信息
    作者简介:

    田 彪(1988–),男,四川南充人,博士,现为国防科技大学电子科学学院副研究员,获省部级科技进步一等奖1项、二等奖1项,全军优秀博士学位论文获得者,国防科技大学青年创新奖获得者。主要研究方向为ISAR成像、目标识别等。E-mail: tbncsz@126.com

    刘 洋(1986–),男,江西樟树人,博士,现为西安卫星测控中心高级工程师,主要研究方向为空间目标监视、目标跟踪与识别等。E-mail: liuyangjiangxi@163.com

    呼鹏江(1990–),男,河南安阳人,博士,现为国防科技大学电子对抗学院讲师,主要研究方向为雷达成像、电子对抗等。E-mail: pjhu2012@126.com

    吴文振(1992–),男,山东枣庄人,硕士,现为国防科技大学电子科学学院博士生,主要研究方向为ISAR成像、抗干扰等。E-mail: idminghai@163.com

    徐世友(1978–),男,河北承德人,博士,现为中山大学电子与通信工程学院教授,博士生导师,主要研究方向为宽带雷达成像、自动目标识别、信息融合、多功能数字阵列雷达等。E-mail: xushy36@mail.sysu.edu.cn

    陈曾平(1967–),男,福建福清人,现为中山大学电子与通信工程学院院长,教授,博士生导师,主要从事空间态势感知、软件化雷达探测、宽带成像识别等。E-mail: chenzengp@mail.sysu.edu.cn

    通讯作者:

    田彪 tbncsz@126.com

    呼鹏江 pjhu2012@126.com

  • 责任主编:许小剑 Corresponding Editor: XU Xiaojian
  • 中图分类号: TN958

Review of High-resolution Imaging Techniques of Wideband Inverse Synthetic Aperture Radar

Funds: The National Natural Science Foundation of China (61901481, 61921001), Hunan Provincial Natural Science Foundation of China (2019JJ50715), Anhui Provincial Natural Science Foundation of China (2008085QF283), China Postdoctoral Science Foundation (2019TQ0074), Scientific research projects of National University of Defense Technology (ZK18-03-58)
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  • 摘要: 当前,国内外逆合成孔径雷达(ISAR)系统均朝着高载频、大带宽、多极化、分布式、网络化的方向发展,并牵引ISAR成像技术的发展和进步。从ISAR图像的角度来看,ISAR成像的发展变化主要可归纳为精细化成像以提升成像质量和多维度成像以丰富成像信息两个方面。该文首先从雷达回波脉冲压缩、雷达系统失真校正、目标高速运动补偿、距离向自聚焦、平动补偿、转动补偿、图像重构、图像后处理等方面综述雷达精细化成像方法,然后从极化、多频带融合、多站多视角成像、三维成像等方面综述雷达成像维度的扩展,最后从成像建模、复杂场景精细成像、实时成像、成像评价与图像应用等4个方面进行展望分析。

     

  • 图  1  两种不同脉压方式流程图

    Figure  1.  Flow chart of two different pulse compression methods

    图  2  理想幅相特性与实际幅相特性的对比

    Figure  2.  Comparison of amplitude and phase

    图  3  塔源实测DIFS回波系统失真补偿试验结果[7]

    Figure  3.  The distortion correction performance of real calibration tower signal[7]

    图  4  飞机目标仿真数据幅相失真补偿实验结果[8]

    Figure  4.  The distortion correction performance of simulated airplane signal[8]

    图  5  国际空间站高速运动补偿效果[15]

    Figure  5.  High velocity movement compensation performance of ISS[15]

    图  6  卫星目标仿真数据高速运动补偿效果[16]

    Figure  6.  High velocity movement compensation performance of simulated satellite[16]

    图  7  距离相位误差补偿效果[18]

    Figure  7.  Range phase error compensation performance[18]

    图  8  空变相位补偿效果[20]

    Figure  8.  Spatial-variant contrast maximization autofocus performance[20]

    图  9  相参化运动补偿效果[22]

    Figure  9.  Coherent motion compensation performance[22]

    图  10  信噪比–5 dB时空间旋转目标的平动补偿效果[26]

    Figure  10.  Translational motion compensation performance of spatial rotation target of SNR=–5 dB[26]

    图  11  相位对消转角估计效果[27]

    Figure  11.  Rotation angle estimation performance by phase cancellation method[27]

    图  12  Mig-25仿真数据MTRC补偿效果[31]

    Figure  12.  MTRC compensation performance of simulated Mig-25 data[31]

    图  13  改进RID算法成像效果[39]

    Figure  13.  Imaging performance of improved RID algorithm[39]

    图  14  基于峰值提取的成像算法效果[45]

    Figure  14.  Imaging performance based on peak extraction[45]

    图  15  暗室测量飞机模型的深度学习网络重构结果[59]

    Figure  15.  Imaging performance via deep network of dark room airplane[59]

    图  16  窄带测量结果定标[61]

    Figure  16.  Scaled result via measurement from narrow band[61]

    图  17  空客A320飞机定标结果[66]

    Figure  17.  Scaled result of A320 airplane[66]

    图  18  基于自调焦的ISAR图像增强效果[68]

    Figure  18.  ISAR image enhancement based on auto-adjust[68]

    图  19  图像增强效果比对[69]

    Figure  19.  ISAR image enhancement performance[69]

    图  20  不同伪彩色编码变换函数及显示效果[70]

    Figure  20.  Transformation functions and display performance of different pseudocolor codes[70]

    图  21  基于Hot Spot的全极化成像效果[74]

    Figure  21.  Full polarization imaging based on Hot Spot[74]

    图  22  信噪比10 dB情况下全极化成像效果[76]

    Figure  22.  Full polarization imaging results when SNR=10 dB[76]

    图  23  基于极化白化滤波的融合成像结果[77]

    Figure  23.  Fusion imaging results based on polarization whitening filtering[77]

    图  24  包络对齐结果对比[78]

    Figure  24.  Comparison of range alignment performance[78]

    图  25  极化成像效果对比[79]

    Figure  25.  Comparison of polarization imaging performance[79]

    图  26  林肯实验室稀疏频带融合成像暗室实验结果[80]

    Figure  26.  Sparse band fusion imaging performance of Lincoln Laboratory[80]

    图  27  极点估计[80]

    Figure  27.  Pole estimation[80]

    图  28  相干化处理结果[82]

    Figure  28.  Coherent processing[82]

    图  29  光滑锥体的电磁仿真数据融合成像结果[84]

    Figure  29.  Fusion imaging results of electromagnetic simulation data of smooth cone[84]

    图  30  Yak-42飞机稀疏频带融合成像结果[91]

    Figure  30.  Fusion imaging results of Yak-42 airplane[91]

    图  31  卫星目标稀疏频带融合成像结果[89]

    Figure  31.  Fusion imaging results of simulated satellite[89]

    图  32  林肯实验室双多基地空间目标雷达跟踪与成像系统示意图[96]

    Figure  32.  Bistatic and multistatic space target radar tracking and imaging system in Lincoln Laboratory[96]

    图  33  单基地与双基地成像结果对比[103]

    Figure  33.  Imaging results comparison of monostatic and bistatic[103]

    图  34  不同姿态下ISAR图像融合结果[104]

    Figure  34.  Fusion ISAR imaging results of different attitude[104]

    图  35  优化布站ISAR图像融合结果[105]

    Figure  35.  Fusion ISAR imaging results of optimum distribution[105]

    图  36  分布式融合结果[106]

    Figure  36.  Fusion ISAR imaging results of distributed system[106]

    图  37  不同方法匹配效果对比[121]

    Figure  37.  Comparison of different matching methods[121]

    图  38  MCMC散射中心关联结果[124]

    Figure  38.  Scattering center correlation results of MCMC[124]

    图  39  提取目标的轮廓特征并关联[127]

    Figure  39.  Extract the contour feature extraction and association[127]

    图  40  空间卫星目标重构结果[133]

    Figure  40.  Reconstruction result of space satellite[133]

    图  41  用网格法匹配然后进行序贯重构图[135]

    Figure  41.  Matching with grid method and sequential reconstruction results[135]

    图  42  基于雷达光学融合的三维重构效果[136]

    Figure  42.  3D reconstruction performance based on radar and optical fusion[136]

    图  43  干涉ISAR系统及成像结果[137]

    Figure  43.  InISAR system and imaging results[137]

    图  44  联合处理的干涉ISAR实测数据处理结果[138]

    Figure  44.  InISAR imaging results by combined processing[138]

    图  45  两雷达联合包络对齐

    Figure  45.  Range alignment by combined processing

    图  46  斜视校正效果[140]

    Figure  46.  Squint model InISAR imaging results[140]

    图  47  阵列ISAR三维成像效果[144]

    Figure  47.  3D imaging performance of array ISAR[144]

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  • 收稿日期:  2020-05-13
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