基于扰动的结合Off-grid目标的层析SAR三维成像方法

杜邦 仇晓兰 张柘 雷斌 丁赤飚

杜邦, 仇晓兰, 张柘, 等. 基于扰动的结合Off-grid目标的层析SAR三维成像方法[J]. 雷达学报, 2022, 11(1): 62–70. doi: 10.12000/JR21093
引用本文: 杜邦, 仇晓兰, 张柘, 等. 基于扰动的结合Off-grid目标的层析SAR三维成像方法[J]. 雷达学报, 2022, 11(1): 62–70. doi: 10.12000/JR21093
DU Bang, QIU Xiaolan, ZHANG Zhe, et al. L1 minimization with perturbation for off-grid tomographic SAR imaging[J]. Journal of Radars, 2022, 11(1): 62–70. doi: 10.12000/JR21093
Citation: DU Bang, QIU Xiaolan, ZHANG Zhe, et al. L1 minimization with perturbation for off-grid tomographic SAR imaging[J]. Journal of Radars, 2022, 11(1): 62–70. doi: 10.12000/JR21093

基于扰动的结合Off-grid目标的层析SAR三维成像方法

doi: 10.12000/JR21093
基金项目: 国家自然科学基金(61991421, 61991420)
详细信息
    作者简介:

    杜 邦(1995–),男,中国科学院空天信息创新研究院在读博士,研究方向为阵列信号处理、SAR三维成像

    仇晓兰(1982–),女,中国科学院空天信息创新研究院研究员,博士生导师,IEEE高级会员、IEEE地球科学与遥感快报副主编、雷达学报青年编委。主要研究方向为SAR成像处理、SAR图像理解

    张 柘(1988–),男,博士,中国科学院空天信息研究院、苏州空天信息研究院副研究员,硕士生导师,研究方向为稀疏信号处理与合成孔径雷达成像

    雷 斌(1978–),男,中国科学院空天信息创新研究院研究员,博士生导师,主要研究方向为合成孔径雷达及多源遥感信息处理与应用系统等

    丁赤飚(1969–),男,研究员,博士生导师,中国科学院院士,先后主持多项国家重点项目和国家级遥感卫星地面系统工程建设等项目,曾获国家科技进步奖一等奖、二等奖,国家技术发明二等奖等奖励。研究方向为合成孔径雷达、遥感信息处理和应用系统等领域

    通讯作者:

    仇晓兰 xlqiu@mail.ie.ac.cn

  • 责任主编:廖明生 Corresponding Editor: LIAO Mingsheng
  • 中图分类号: TN957.52

L1 Minimization with Perturbation for Off-grid Tomographic SAR Imaging

Funds: The National Natural Science Foundation of China (61991421, 61991420)
More Information
  • 摘要: 层析合成孔径雷达(TomoSAR)通过组合在不同高度上获取的多基线二维SAR数据,实现合成孔径雷达的三维成像。TomoSAR的求解本质是一维谱估计问题,基于压缩感知的方法可以在非均匀分布的少量基线观测下实现求解,逐渐成为了主流的成像方式。在经典的压缩感知算法流程中,需要将连续的高程向划分成固定的网格,并且假定目标正好位于所划分的网格上。然而该假设通常难以成立,从而引起“基失配”问题,目前该问题在TomoSAR中很少被讨论。该文首先讨论了目标不在网格(Off-grid)上的TomoSAR观测模型,提出了采用加性扰动项来修正目标偏离网格所带来影响的求解模型。在此基础之上,引入局部优化算法与$ {L}_{1} $范数最小化结合的方法,求解所提出的Off-grid TomoSAR模型。最后,利用仿真数据和机载阵列干涉SAR实际飞行数据进行了验证,结果表明,对于Off-grid目标,该方法能够得到比基于$ {L}_{1} $范数最小化的经典方法更准确的位置、幅度和相位求解结果,证明了方法的优越性。

     

  • 图  1  TomoSAR成像几何关系

    Figure  1.  The geometry of TomoSAR imaging

    图  2  高程向目标重建结果的简单示意图

    Figure  2.  Sketch map of the reconstruct results in the elevation direction

    图  3  点目标仿真结果

    Figure  3.  Point target simulation results

    图  4  散射点位置估计值与真值距离的统计平均值

    Figure  4.  The average distance between the estimated scattering point position and the true value

    图  5  本文方法优于传统方法的概率与SNR的关系

    Figure  5.  Probability of the proposed algorithm providing better estimation versus SNR

    图  6  获得更精确估计的概率与观测数量的关系

    Figure  6.  Probability of the proposed algorithm providing better estimation versus number of acquisitions

    图  7  RMSE的计算结果

    Figure  7.  RMSE results

    图  8  从谷歌地球获取的实验地区于2015年的光学影像和对应的SAR图像

    Figure  8.  Optical image of the experiment area obtained from Google Earth in 2015 and SAR image of the corresponding area

    图  9  图8(a)中最后一排建筑光学影像

    Figure  9.  Optical image of the last row of buildings Fig. 8(a)

    图  10  图9中红框建筑的三维重建结果

    Figure  10.  TomoSAR results of the building marked in Fig. 9

    图  11  本文方法与传统L1范数最小化的方法对图8中红线标记的方位向切片的成像结果比较

    Figure  11.  Comparison of our method and the conventional process of $ {L}_{1} $ minimization in the slant range plane for the azimuth-bin marked in Fig. 8

    图  12  图11中区域的放大图

    Figure  12.  Zoom in of the circled area in Fig. 11

    表  1  BPLOT的流程

    Table  1.   The process of BPLOT

     算法 : BPLOT
     (1) 输入: $\boldsymbol{A},{\boldsymbol{A} }',\boldsymbol{g},\Delta s{\text{,}}\mathrm{稀}\mathrm{疏}\mathrm{度}K$
     (2) $ {L}_{1} $范数求解:
       $\mathrm{argmin} \left\{ {\left|\left|{ { {\boldsymbol{g} } } }-{{ {\boldsymbol{A} } } }_{\mathit{g} }{\boldsymbol\varGamma }\right|\right|}_{2}^{2}+\mu {\left|\left|{\boldsymbol\varGamma }\right|\right|}_{1}\right\}$
       $\boldsymbol{\varGamma }={\left[\boldsymbol{\gamma }\;\;{\boldsymbol{\gamma } }'\right]}^{t},{\boldsymbol{A} }_{\mathit{g} }=\left[\begin{array}{cc}\boldsymbol{A}& {\boldsymbol{A} '} \end{array}\right]$
     (3) For count t=1: K,
     (4) $ {\alpha }_{t} $=${\mathrm{a}\mathrm{r}\mathrm{g}\mathrm{m}\mathrm{a}\mathrm{x} }_{p}\{\left|{\mathit{\gamma } }_{p}\right|+\left|{\mathit{\gamma } }_{p}\right|/|2\pi {\Delta }s\left|\right\}$, $ p\notin \left({S}^{t-1}\right) $
     (5) $ {S}^{t}={S}^{t-1}\cup \left\{{\alpha }_{t}\right\} $
     (6) 计算网格偏差 $\Delta k\Delta s=\mathfrak{R}e({{\boldsymbol{\gamma}} }'/({\rm{j} }2\pi {\boldsymbol{\gamma}} \left)\right)$
     (7) 计算散射值
       ${\hat{\boldsymbol{A} } }_{f}={{\boldsymbol{A}}}(S+\Delta k\Delta s)$
       ${\hat{\gamma } }_{f}={\left({ {\hat{ \boldsymbol{A} } }_{f} }^{\rm{H} }{\hat{ \boldsymbol{A} } }_{f}\right)}^{-1}{ {\hat{ \boldsymbol{A} } }_{f} }^{\rm{H} }\boldsymbol{g}$
     (8) 输出:${\hat{\gamma } }_{f},S,\Delta k$
    下载: 导出CSV

    表  2  仿真参数

    Table  2.   Simulation parameters

    参数参数值
    载波频率(GHz)14.5
    信号带宽(MHz)500
    通道数N8
    基线宽度(m)0.084
    脉冲重复频率(Hz)480
    平台高度(m)1200
    平台速度(m/s)70
    下视角(°)45
    下载: 导出CSV

    表  3  图3对应的位置计算结果

    Table  3.   Point calculation results corresponding to Fig. 3

    参数真值$ {L}_{1} $BPLOT
    K=2[0.1506, 0.3764][0.1400, 0.3750][0.1506, 0.3764]
    K=3[0.1501, 0.3764, 0.6120][0.1484, 0.3750, 0.6094][0.1501 0.3765 0.6119]
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-01
  • 修回日期:  2021-09-17
  • 网络出版日期:  2021-10-15
  • 刊出日期:  2022-02-28

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