基于分布式压缩感知的高分宽幅SAR动目标成像技术

潘洁 王帅 李道京 卢晓春

潘洁, 王帅, 李道京, 等. 基于分布式压缩感知的高分宽幅SAR动目标成像技术[J]. 雷达学报, 2020, 9(1): 166–173. doi: 10.12000/JR19060
引用本文: 潘洁, 王帅, 李道京, 等. 基于分布式压缩感知的高分宽幅SAR动目标成像技术[J]. 雷达学报, 2020, 9(1): 166–173. doi: 10.12000/JR19060
PAN Jie, WANG Shuai, LI Daojing, et al. High-resolution Wide-swath SAR moving target imaging technology based on distributed compressed sensing[J]. Journal of Radars, 2020, 9(1): 166–173. doi: 10.12000/JR19060
Citation: PAN Jie, WANG Shuai, LI Daojing, et al. High-resolution Wide-swath SAR moving target imaging technology based on distributed compressed sensing[J]. Journal of Radars, 2020, 9(1): 166–173. doi: 10.12000/JR19060

基于分布式压缩感知的高分宽幅SAR动目标成像技术

doi: 10.12000/JR19060
基金项目: 国家高分重大专项共性关键技术(30-H30C01-9004-19/21)
详细信息
    作者简介:

    潘 洁(1977–),女,四川人,中国科学院空天信息创新研究院高级工程师,博士生,研究方向为稀疏阵列雷达系统。E-mail: panjie@aircas.ac.cn

    王 帅(1992–),男,安徽阜阳人,中国科学院空天信息创新研究院博士生,研究方向为合成孔径雷达成像技术。E-mail: wangshuai161@mils.ucas.edu.cn

    李道京(1964–),男,陕西西安人,中国科学院空天信息创新研究院研究员,博士生导师,主要研究方向为雷达系统和雷达信号处理。E-mail: lidj@mail.ie.ac.cn

    卢晓春(1970–),女,中国科学院授时中心研究员,博士生导师,研究方向为精密时间信息传输与信息处理。E-mail: luxc@ntsc.ac.cn

    通讯作者:

    潘洁 panjie@aircas.ac.cn

  • 中图分类号: TN951

High-resolution Wide-swath SAR Moving Target Imaging Technology Based on Distributed Compressed Sensing

Funds: The Key Standard Technologies of National High Resolution Special (30-H30C01-9004-19/21)
More Information
  • 摘要: 高分宽幅SAR动目标成像对目标跟踪具有重要的意义,常规天基多通道SAR技术要实现高分宽幅动目标成像需要通道数量巨大,系统复杂度过高,而且图像在方位向存在成对回波,形成虚警。针对上述问题,该文提出了一种基于分布式压缩感知的高分宽幅SAR动目标成像技术,在通道数量较大时,通道数量相比常规高分宽幅动目标成像构型通道数量约降低1倍,利用动目标稀疏特性和杂波背景非稀疏特性构建分布式压缩感知观测模型,采用先方位1维分布式压缩感知重建再距离方位2维分布式压缩感知重建,实现杂波背景和稀疏动目标的重建,并抑制多通道SAR动目标成像中的成对回波。结合RADAR-SAT数据的仿真试验结果验证了该技术的有效性。

     

  • 图  1  SAR系统坐标系

    Figure  1.  Coordinate system of SAR

    图  2  多通道SAR高分宽幅动目标成像通道构型

    Figure  2.  Channel configuration of multi-channel SAR high resolution wide swath target imaging

    图  3  基于分布式压缩感知的高分宽幅动目标成像通道构型

    Figure  3.  Channel configuration of high resolution wide swath target imaging based on distributed compressed sensing

    图  4  算法流程图

    Figure  4.  Flowchart of the algorithm

    图  5  仿真采用实测图像

    Figure  5.  Measured image used in simulation

    图  6  单通道成像结果

    Figure  6.  Imaging result of single channel

    图  7  方位分布式压缩感知重建结果

    Figure  7.  Reconstructed results of azimuth distributed compressed sensing

    图  8  距离方位分布式压缩感知重建结果

    Figure  8.  Reconstructed results of distributed compressed sensing in range-azimuth

    表  1  算法流程

    Table  1.   Algorithm flowchart

     (1) 对高分宽幅SAR动目标成像系统每个通道数据分别进行距离压缩处理,获得每个通道的1维距离图像;
     (2) 联合多通道数据转换到方位多普勒域进行距离徙动校正,之后转换回到方位时域,完成对静止目标距离徙动校正,而动目标由于相位
       误差存在距离徙动校正误差;
     (3) 对距离徙动校正后的多通道数据构造方位1维分布式压缩感知模型,利用优化方法进行信号重建,分别获得非稀疏背景杂波和稀疏动目
       标散射信息;
     (4) 根据稀疏动目标散射信息估计动目标的位置和速度参数;
     (5) 对距离徙动校正后的多通道数据根据运动目标位置和速度信息构造距离方位2维分布式压缩感知模型,利用优化方法进行信号重建,获
       得非稀疏背景杂波和稀疏动目标散射信息;
     (6) 将运动目标信息${{{\sigma}} _2}$和背景杂波图像${{{\sigma}} _1}$融合形成完整的高分宽幅图像。
    下载: 导出CSV

    表  2  雷达系统主要参数

    Table  2.   Main parameters of radar system

    参数数值
    工作频率5.3 GHz
    带宽30 MHz
    采样率32.3 MHz
    PRF1256.98 Hz
    速度7062 m/s
    通道数3
    动目标速度2.5 m/s
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-14
  • 修回日期:  2019-11-20
  • 网络出版日期:  2020-02-28

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