机载圆周SAR成像技术研究

安道祥 陈乐平 冯东 黄晓涛 周智敏

安道祥, 陈乐平, 冯东, 等. 机载圆周SAR成像技术研究[J]. 雷达学报, 2020, 9(2): 221–242. doi: 10.12000/JR20026
引用本文: 安道祥, 陈乐平, 冯东, 等. 机载圆周SAR成像技术研究[J]. 雷达学报, 2020, 9(2): 221–242. doi: 10.12000/JR20026
AN Daoxiang, CHEN Leping, FENG Dong, et al. Study of the airborne circular synthetic aperture radar imaging technology[J]. Journal of Radars, 2020, 9(2): 221–242. doi: 10.12000/JR20026
Citation: AN Daoxiang, CHEN Leping, FENG Dong, et al. Study of the airborne circular synthetic aperture radar imaging technology[J]. Journal of Radars, 2020, 9(2): 221–242. doi: 10.12000/JR20026

机载圆周SAR成像技术研究

doi: 10.12000/JR20026
基金项目: 国家自然科学基金项目(61571447),国家部委基金,航空科学基金项目(20182088004)
详细信息
    作者简介:

    安道祥(1982–),男,吉林东丰人,博士,现为国防科技大学电子科学学院副教授,主要研究方向为机载低频单/双站超宽带SAR成像、机载CSAR成像、视频SAR成像、重轨InSAR和SAR图像解译等。E-mail: daoxiangan@nudt.edu.cn

    陈乐平(1988–),男,福建福州人,博士,现为国防科技大学电子科学学院讲师,主要研究方向为高分辨率合成孔径雷达成像。E-mail: gfkdclp@126.com

    冯 东(1992–),男,重庆涪陵人,现为国防科技大学电子科学学院博士生,主要研究方向为机载SAR三维成像技术。E-mail: fengdong09@nudt.edu.cn

    黄晓涛(1972–),男,湖北武汉人,现为国防科技大学电子科学学院教授,主要研究方向为阵列信号处理技术

    周智敏(1957–),男,河北易县人,现为国防科技大学电子科学学院教授,主要研究方向为超宽带雷达技术

    通讯作者:

    安道祥 daoxiangan@nudt.edu.cn

  • 责任主编:张晓玲 Corresponding Editor: ZHANG Xiaoling
  • 中图分类号: TN959.4

Study of the Airborne Circular Synthetic Aperture Radar Imaging Technology

Funds: The National Natural Science Foundation of China (61571447), The National Ministries Foundation, The Aviation Science Foundation of China (20182088004)
More Information
  • 摘要:

    机载圆周合成孔径雷达(CSAR)作为一种新兴的成像模式,具有全方位观测、高空间分辨率和可三维成像等优点。随着CSAR成像技术的不断发展,现已逐渐成为对重点区域实施精确观测的有效手段之一。该文重点阐述了作者所在研究团队近年来在机载CSAR成像技术方面完成的研究工作,包括机载CSAR成像模型,空间分辨率评估,CSAR二维成像,基于单圆周CSAR的目标三维图像重构和多基线CSAR(HoloSAR)三维成像等技术,并给出了P, X两个频段机载CSAR的实测数据处理结果。已取得的研究成果证明了机载CSAR成像的有效性和实用性。该文主要内容基于作者2019年8月16日在“雷达学报第五届青年科学家论坛”上的学术报告。

     

  • 图  1  机载CSAR成像几何

    Figure  1.  Airborne CSAR imaging geometry

    图  2  CSAR成像的波数谱支撑图

    Figure  2.  The supported wavenumber spectrum of CSAR imaging

    图  4  不同子孔径积累角下的分辨率变化曲线

    Figure  4.  Investigation of the resolutions with respect to the different subaperture angles

    图  3  不同子孔径积累角与相对带宽比下的波数展宽因子仿真结果

    Figure  3.  The simulated results of HPBW factor versus to the different fractional bandwidths and subaperture angles

    图  5  机载CSAR自聚焦处理流程图

    Figure  5.  The flow chart of airborne CSAR autofocus processing

    图  6  试验平台

    Figure  6.  Experiment platform

    图  7  机载P波段CSAR成像结果

    Figure  7.  The imaging result of airborne P band CSAR

    图  8  机载P波段LSAR和CSAR成像结果对比

    Figure  8.  Comparison of airborne P-band LSAR and CSAR imaging results

    图  9  机载P波段CSAR图像的局部放大图

    Figure  9.  Partial enlarged view of airborne P-band CSAR image

    图  10  “顶底平移”计算示意图

    Figure  10.  The schematic diagram of“Layover”calculation

    图  11  偶反射的几何路径

    Figure  11.  The geometry of the even-bounce reflection

    图  12  基于Gotcha实测数据的车辆CSAR成像

    Figure  12.  The imaging results of a vehicle in Gotcha public release dataset

    图  13  基于单圆周CSAR的车辆目标三维图像重构流程

    Figure  13.  The flowchart of the 3D reconstruction of vehicle’s outline based single-pass CSAR

    图  14  Gotcha数据及CSAR成像结果

    Figure  14.  The Gotcha data and its corresponding CSAR imaging result

    图  15  车辆照片(上排)和对应的三维图像重构结果(下排)

    Figure  15.  The vehicle photos (upper rows) and their corresponding 3D image reconstruction results (lower rows)

    图  16  机载HoloSAR三维成像几何构型

    Figure  16.  The airborne HoloSAR 3D imaging geometry

    图  17  机载HoloSAR三维成像处理流程

    Figure  17.  The airborne HoloSAR 3D imaging processing flow

    图  18  Gotcha数据中雷达平台的实际运动轨迹

    Figure  18.  The actual trajectory of the radar platform in Gotcha data

    图  19  车辆C1的实物照片及三维成像结果

    Figure  19.  The photo of vehicle C1 and its 3D imaging result

    图  20  车辆C1三维成像结果在不同平面上的二维投影图像

    Figure  20.  The two-dimensional projected images of the three-dimensional imaging results on different planes

    图  21  车辆B的HoloSAR三维成像结果

    Figure  21.  The HoloSAR 3D imaging results of vehicle B

    图  22  车辆F的HoloSAR三维成像结果

    Figure  22.  The HoloSAR 3D imaging results of vehicle F

    图  23  车辆C2的HoloSAR三维成像结果

    Figure  23.  The HoloSAR 3D imaging results of vehicle C2

    表  1  车辆的真实尺寸与估计值对比(mm)

    Table  1.   The comparisons between the actual size of the vehicles and their estimated values

    车辆编号(品牌)
    $l$$\hat l$$\Delta l$$w$$\hat w$$\Delta w$$h$$\hat h$$\Delta h$
    车辆A(Chevy Malibu)48404814261760161614414301433–3
    车辆B(Toyota Camry)479047108017801711691410133278
    车辆C(Ford Taurus)502048731471850173711314701482–12
    车辆D(Nissan Maxima)47704834–6417701687831410137139
    车辆E(Nissan Sentra)451042083021710161010014401459–19
    车辆F(Hyundai Santa Fe)45004517–17184017716916701684–14
    车辆J(Chevy Prizm)44204226194169014472431360133921
    误差均值和标准差${\mu _{\Delta l} } = 95,\; {\sigma _{\Delta l} } = 128$${\mu _{\Delta w} } = {\rm{117} }, \;{\sigma _{\Delta w} } = {\rm{61} }$${\mu _{\Delta h} } = {\rm{13} },\; {\sigma _{\Delta h} } = {\rm{36} }$
    下载: 导出CSV

    表  2  车辆的真实尺寸与估计值(mm)

    Table  2.   Comparison of the actual size of the vehicle with the estimated value

    车辆编号(品牌)
    $l$$\hat l$$\Delta l$$w$$\hat w$$\Delta w$$h$$\hat h$$\Delta h$
    车辆A(Chevy Malibu)4840480040176017501014301450–20
    车辆B(Toyota Camry)47904800–1017801800–201410140010
    车辆C(Ford Taurus)50205050–3018501850014701480–10
    车辆D(Nissan Maxima)477047502017701800–301410139020
    车辆E(Nissan Sentra)45104350160171014502601440143010
    车辆F(Hyundai Santa Fe)45004500018401850–1016701680–10
    车辆J(Chevy Prizm)4420430012016901600901360132040
    误差均值和标准差${\mu _{\Delta l} } = 40,\; {\sigma _{\Delta l} } = 70$${\mu _{\Delta w} } = {\rm{40} },\; {\sigma _{\Delta w} } = 100$${\mu _{\Delta h} } = {\rm{10} },\; {\sigma _{\Delta h} } = 20$
    下载: 导出CSV
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  • 收稿日期:  2020-03-31
  • 修回日期:  2020-04-24
  • 网络出版日期:  2020-04-01

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