基于子图像变标的非线性轨迹SAR成像及其自聚焦方法

陈溅来 熊毅 徐刚 张俊超 杨德贵 梁步阁

陈溅来, 熊毅, 徐刚, 等. 基于子图像变标的非线性轨迹SAR成像及其自聚焦方法[J]. 雷达学报, 2022, 11(6): 1098–1109. doi: 10.12000/JR22171
引用本文: 陈溅来, 熊毅, 徐刚, 等. 基于子图像变标的非线性轨迹SAR成像及其自聚焦方法[J]. 雷达学报, 2022, 11(6): 1098–1109. doi: 10.12000/JR22171
CHEN Jianlai, XIONG Yi, XU Gang, et al. Nonlinear trajectory synthetic aperture radar imaging and autofocus algorithm based on sub-image nonlinear chirp scaling[J]. Journal of Radars, 2022, 11(6): 1098–1109. doi: 10.12000/JR22171
Citation: CHEN Jianlai, XIONG Yi, XU Gang, et al. Nonlinear trajectory synthetic aperture radar imaging and autofocus algorithm based on sub-image nonlinear chirp scaling[J]. Journal of Radars, 2022, 11(6): 1098–1109. doi: 10.12000/JR22171

基于子图像变标的非线性轨迹SAR成像及其自聚焦方法

doi: 10.12000/JR22171
基金项目: 国家自然科学基金(61901531, 62271510, 62105372, 62171475)
详细信息
    作者简介:

    陈溅来,副教授,博士生导师,主要研究方向为雷达成像、雷达图像解译

    熊 毅,博士生,主要研究方向为非线性轨迹SAR成像

    徐 刚,副教授,博士生导师,主要研究方向为雷达成像技术、遥感图像处理、稀疏信号处理、统计机器学习、人工智能等

    张俊超,讲师,主要研究方向为光电信息处理、模式识别和机器学习

    杨德贵,教授,博士生导师,主要研究方向为目标特性分析、雷达电子对抗及雷达目标探测与识别

    梁步阁,教授,博士生导师,主要研究方向为超宽带雷达探测技术、电磁性能集成测试

    通讯作者:

    陈溅来 jianlaichen@163.com

    熊毅 XY639692@163.com

  • 责任主编:仇晓兰 Corresponding Editor: QIU Xiaolan
  • 中图分类号: TN955

Nonlinear Trajectory Synthetic Aperture Radar Imaging and Autofocus Algorithm Based on Sub-image Nonlinear Chirp Scaling

Funds: The National Natural Science Foundation of China (61901531, 62271510, 62105372, 62171475)
More Information
  • 摘要: 合成孔径雷达(SAR)的非线性轨迹运动可能会在雷达回波信号中引入严重的二维空变特性,因此基于方位平移不变性假设的传统频域成像算法不再适用于非线性轨迹SAR的高精度成像。现有非线性轨迹SAR成像算法通常采用复杂的非线性变标(NCS)校正回波信号的方位空变特性,然而NCS参数过多导致算法复杂,使得其当存在较大平台运动测量误差时无法与现有自聚焦算法有效结合。针对该问题,该文提出一种基于子图像NCS的非线性轨迹SAR成像及其自聚焦方法,在保证成像精度的前提下能够减少NCS的参数数量,更有利于后续的自聚焦处理。仿真与实测数据处理验证了所提方法的有效性。

     

  • 图  1  双基非线性轨迹SAR几何构型

    Figure  1.  Geometric configuration of bistatic nonlinear trajectory SAR

    图  2  所提算法流程图

    Figure  2.  Flowchart of the proposed algorithm

    图  3  2次误差的2阶空变特性校正示意图

    Figure  3.  Schematic diagram for the second-order spatial-variant characteristic correction of quadratic error

    图  4  剩余空变相位随子图像数量的变化

    Figure  4.  Change of residual spatial-variant phase with the number of sub images

    图  5  场景左端点目标仿真数据处理结果

    Figure  5.  The simulation data processing results of the left edge target in the scene

    图  6  场景右端点目标仿真数据处理结果

    Figure  6.  The simulation data processing results of the right edge target in the scene

    图  7  位置偏移分析结果

    Figure  7.  Analysis results of position offset

    图  8  实测数据处理结果(第1列为第2列左端红色虚线矩形框的局部细节图;第3列为第2列右端红色虚线矩形框的局部细节图)

    Figure  8.  Measured data processing results (The first and the third columns are the local enlarged images of the left and right edge scenes, respectively)

    表  1  仿真参数

    Table  1.   Simulation parameters

    参数发射机接收机
    初速度(48.3, 13.6, 0) m/s(48.2, 13.5, 0) m/s
    加速度(–0.05, –0.01, 0) m/s2(–0.05, –0.01, 0) m/s2
    载频16 GHz
    PRF1000 Hz
    带宽1400 MHz
    合成孔径时间24 s
    方位分辨率0.1 m
    最近斜距17.5 km
    工作模式聚束
    斜视角0o
    下载: 导出CSV

    表  2  3种方法方位聚焦质量的定量评估结果

    Table  2.   Quantitative evaluation results of azimuth focusing quality for the three methods

    方法场景左端目标场景右端目标
    PSLR(dB)ISLR(dB)熵值PSLR(dB)ISLR(dB)熵值
    匹配滤波–2.34–5.118.487–3.47–4.888.512
    NCS[18]–5.27–7.248.391–11.48–8.918.344
    子图像分割+NCS–13.14–9.818.312–13.20–9.898.309
    下载: 导出CSV

    表  3  变标函数系数${\boldsymbol{{\alpha _k},\beta}}$的估计算法

    Table  3.   The estimation method of coefficients ${\boldsymbol{{\alpha _k},\beta}}$

     步骤1 求解第1个最优化问题,即式(17)
         1.1 输入:two-step MoCo后的SAR图像
         1.2 设$ \alpha = 0 $,参数$ \beta $的初始搜索区间为$\beta \in \left[ { {\beta _{\rm{s}}},{\beta _{\rm{e}}} } \right]$,迭       代终止阈值为$ \varepsilon {\text{ = }}0.01 $
         1.3 while $\max \left\{ {E\left( { {\beta _{\rm{s} } } } \right),E\left( { {\beta _{\rm{e} } } } \right)} \right\} > \varepsilon$
           引入式(8)中的4阶变标函数;采用黄金分割法不断       缩小$ \beta $的区间
         1.4 End while
         1.5 输出:$ \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\beta } $及3阶误差校正后的图像
     步骤2 求解第2个最优化问题,即式(18)
         2.1 输入:$ \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\beta } $及3阶误差校正后的图像
         2.2 对图像进行分块处理,得到N个子图像
         2.3 For k = 1:N
           (1) 设$ \beta = \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\beta } $,参数$ {\alpha _k} $的初始搜索区间为
           ${\alpha _k} \in \left[ { {\alpha _{ {\rm{sk} } } } ,{ {\alpha _{ {\rm{ek} } } } } } \right]$,迭代终止阈值为$ \varepsilon {\text{ = }}0.01 $
           (2) while $\max \left\{ { {E_k}\left( { {\alpha _{{\rm{sk}}} } } \right),{E_k}\left( { {\alpha _{{\rm{ek}}} } } \right)} \right\} > \varepsilon$
           引入式(7)中的3阶变标函数;采用黄金分割法不断       缩小$ {\alpha _k} $的区间
           (3) End while
           (4) 输出:$ {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{\alpha } _k} $及2阶误差校正后的子图像
         2.4 End
         2.5 子图像拼接
    下载: 导出CSV

    表  4  图8局部细节图的熵值评估结果

    Table  4.   Entropy evaluation results of local enlarged images in Fig. (8)

    方法场景左端场景右端
    匹配滤波11.28710.943
    NCS[18]11.16110.867
    子图像分割+NCS11.16010.852
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-08-23
  • 修回日期:  2022-11-16
  • 网络出版日期:  2022-11-24
  • 刊出日期:  2022-12-28

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