一种基于二维信号稀疏重构的互质采样星载SAR成像处理方法

赵婉婉 王鹏波 门志荣 李春升

赵婉婉, 王鹏波, 门志荣, 等. 一种基于二维信号稀疏重构的互质采样星载SAR成像处理方法[J]. 雷达学报, 2020, 9(1): 131–142. doi: 10.12000/JR19086
引用本文: 赵婉婉, 王鹏波, 门志荣, 等. 一种基于二维信号稀疏重构的互质采样星载SAR成像处理方法[J]. 雷达学报, 2020, 9(1): 131–142. doi: 10.12000/JR19086
ZHAO Wanwan, WANG Pengbo, MEN Zhirong, et al. Imaging method for Co-prime-sampling space-borne SAR based on 2D sparse-signal reconstruction[J]. Journal of Radars, 2020, 9(1): 131–142. doi: 10.12000/JR19086
Citation: ZHAO Wanwan, WANG Pengbo, MEN Zhirong, et al. Imaging method for Co-prime-sampling space-borne SAR based on 2D sparse-signal reconstruction[J]. Journal of Radars, 2020, 9(1): 131–142. doi: 10.12000/JR19086

一种基于二维信号稀疏重构的互质采样星载SAR成像处理方法

doi: 10.12000/JR19086
基金项目: 国家自然科学基金(61628101),上海航天科技创新基金(SAST2016029)
详细信息
    作者简介:

    赵婉婉(1992–),女,山西临汾人,硕士,研究方向为新体制合成孔径雷达成像。E-mail: zhao_ww@buaa.edu.cn

    王鹏波(1979–),男,江西波阳人,副教授,研究方向为新体制合成孔径雷达成像,高分辨率雷达成像处理技术,多元信息融合技术。E-mail: wangpb7966@buaa.edu.cn

    门志荣(1988–),男,山东莱西人,博士后,研究方向为新体制合成孔径雷达技术。E-mail: menzhirong@buaa.edu.cn

    李春升(1963–),男,天津人,教授,博士生导师,研究方向为星载SAR系统总体与仿真、多源遥感图像信息融合、信息获取与处理等。E-mail: chunshengli201@163.com

    通讯作者:

    王鹏波 wangpb7966@buaa.edu.cn

  • 中图分类号: TN957.52

Imaging Method for Co-prime-sampling Space-borne SAR Based on 2D Sparse-signal Reconstruction

Funds: The National Natural Science Foundation of China (61628101), The Innovation Foundation of Aerospace Science and Technology of Shanghai (SAST2016029)
More Information
  • 摘要: 互质采样星载SAR通过方位互质采样代替传统方位均匀采样,可有效缓解空间分辨率与有效成像宽度之间的相互制约,提升SAR系统的对地探测性能。然而,方位向互质采样使得回波信号呈现方位欠采样及非均匀采样特性,导致传统SAR成像处理方法无法实现互质采样星载SAR的有效成像处理。该文提出一种基于2维信号稀疏重构的互质采样星载SAR成像处理方法。该方法在距离向脉冲压缩后,根据各距离门的多普勒参数截取2维观测信号并构造相应的稀疏字典,然后通过改进的2维信号稀疏度自适应匹配追踪算法完成方位聚焦处理。该方法不仅可以补偿SAR回波信号的距离方位2维耦合,还可以消除成像参数随距离空变对稀疏重构造成的影响,从而实现全场景的精确重构。点目标及分布目标仿真实验结果验证了所提算法可在远低于奈奎斯特采样率的情况下实现稀疏场景的有效重构。

     

  • 图  1  (M, N)-互质阵列物理阵元分布

    Figure  1.  Structure of an (M, N)-co-prime array

    图  2  互质采样星载SAR对地观测示意图

    Figure  2.  Schematic diagram of co-prime-sampling spaceborne SAR

    图  3  星载SAR空间几何关系

    Figure  3.  Geometry of a typical space-borne SAR

    图  4  在距离向聚焦回波信号上截取2维观测信号

    Figure  4.  A 2-D observed signal is intercepted form the range-focused signal

    图  5  基于2维信号稀疏重构的互质采样星载SAR成像处理方法流程图

    Figure  5.  General flow chart of the imaging method based on 2D sparse signal reconstruction for co-prime-sampling spaceborne SAR

    图  6  改进的2维信号SAMP算法框图

    Figure  6.  Block diagram of the improved 2D SAMP algorithm

    图  7  含有相同有效回波位置的原子的感知矩阵

    Figure  7.  A sensing matrix containing atoms with the same effective echo position.

    图  8  点目标重构结果

    Figure  8.  Reconstruction results of point targets

    图  9  各点目标的3维轮廓图

    Figure  9.  3D contour map of each point target

    图  10  多点目标重构结果

    Figure  10.  Reconstruction results of multiple point targets

    图  11  各行点目标重构幅度误差和相位误差曲线

    Figure  11.  The amplitude error curve and phase error curve of the point targets in the reconstruction result

    图  12  TerraSAR-X Level1 SSC产品图像

    Figure  12.  TerraSAR-X Level1 SSC product image

    图  13  海面场景目标成像仿真结果

    Figure  13.  Sea surface target imaging simulation result

    表  1  改进的SAMP算法

    Table  1.   The modified 2D SAMP algorithm

     输入:感知矩阵${{ A}} = {{\varPhi \varPsi }}$,观测信号${{y}}$,步长$l$;
     输出:输入观测信号的1维稀疏表示向量估计${{\hat{ \theta }}_{N \times 1}}$;
     拉长:将观测信号${{y}}$和感知矩阵$ {{ A}}$中所有原子均拉长为一个1维列向量;
     初始化:${{{r}}_0} = {{y}}$, ${{\varLambda }_0} = \varnothing $, ${L_y} = l$, ${L_r} = l$, $t = 1$, ${{\hat{ \theta }}_{N \times 1}} = \left[ {0,0, ··· ,0} \right]_{_{1 \times N}}^{\rm{T}}$;
     预测试1:选择感知矩阵$ {{ A}}$中有效回波位置正确的原子加入初选集$P$;
     重复下述循环:
       预测试2:$S_{yt}^{} = {\rm{Max}}\left\{ {\left| {{{ A}}_P^{\rm{H}}{{y}}} \right|,{L_y}} \right\}$, $S_{rt}^{} = {\rm{Max}}\left\{ {\left| {{{ A}}_P^{\rm{H}}{{{r}}_{t - 1}}} \right|,{L_r}} \right\}$, ${C_t} = {{\varLambda }_{t - 1}} \cup {S_{yt}} \cup {S_{rt}}$;
       最终测试:${F_t} = {\rm{Max}}\left\{ {\left| {{{\left( {{{A}}_{{C_t}}^{\rm{H}}{{{A}}_{{C_t}}}} \right)}^{{\rm{ - 1}}}}{{A}}_{{C_t}}^{\rm{H}}{{y}}} \right|,{L_y}} \right\}$;
       计算:${{{r}}_{{\rm{new}}}} = {{y}} - {{{A}}_{{F_t}}}{\left( {{{A}}_{{F_t}}^{\rm{H}}{{{A}}_{{F_t}}}} \right)^{{\rm{ - 1}}}}{{A}}_{{F_t}}^{\rm{H}}{{y}}$, ${\rm{rat}}{{\rm{e}}_{\rm{0}}} = \;\;{{\left( {{{\left\| {{{{r}}_0}} \right\|}_2} - {{\left\| {{{{r}}_{{\rm{new}}}}} \right\|}_2}} \right)} / {{{\left\| {{{{r}}_0}} \right\|}_2}}}$, ${\rm{rat}}{{\rm{e}}_{\rm{1}}} = \;{{\left( {{{\left\| {{{{r}}_{t - 1}}} \right\|}_2} - {{\left\| {{{{r}}_{{\rm{new}}}}} \right\|}_2}} \right)} / {{{\left\| {{{{r}}_0}} \right\|}_2}}}$;
       判断:if ${\rm{rat}}{{\rm{e}}_{\rm{0}}} > 1 - {\varepsilon _0}$:
           ${F_{{\rm{final}}}} = {F_t}$, quit;
          else:
           if ${\rm{rat}}{{\rm{e}}_{\rm{1}}} < 0$:
            ${L_y} = {L_y} + l$, ${L_r} = {L_r} + l$;
           else:
            ${L_y} = {L_y} + l$, ${L_r} = l$, ${{\varLambda }_t} = {F_t}$, ${{{r}}_t} = {{{r}}_{{\rm{new}}}}$, $t = t + 1$,
            if ${\rm{rat}}{{\rm{e}}_{\rm{1}}} > {\varepsilon _1}$:
             ${F_{{\rm{final}}}} = {F_t}$;
            else: quit;
     结束循环条件:$t = M$;
     输出:${{\hat{ \theta }}_{N \times 1}} = {{{A}}_{{F_{{\rm{final}}}}}}{\left( {{{A}}_{{F_{{\rm{final}}}}}^{\rm{H}}{{{A}}_{{F_{{\rm{final}}}}}}} \right)^{{\rm{ - 1}}}}{{A}}_{{F_{{\rm{final}}}}}^{\rm{H}}{{y}}$
    下载: 导出CSV

    表  2  SAR成像仿真参数

    Table  2.   SAR imaging simulation parameters

    参数
    轨道高度 (km)800
    发射信号载频 (GHz)10
    发射信号脉宽 (μs)30
    发射信号带宽 (MHz)60
    距离向采样率 (MHz)72
    方位向天线长度 (m)9
    脉冲重复频率 (Hz)2000
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-23
  • 修回日期:  2019-11-23
  • 网络出版日期:  2020-02-28

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