基于稀疏贝叶斯正则化的阵列SAR高分辨三维成像算法

闫敏 韦顺军 田博坤 张晓玲 师君

闫敏, 韦顺军, 田博坤, 张晓玲, 师君. 基于稀疏贝叶斯正则化的阵列SAR高分辨三维成像算法[J]. 雷达学报, 2018, 7(6): 705-716. doi: 10.12000/JR18067
引用本文: 闫敏, 韦顺军, 田博坤, 张晓玲, 师君. 基于稀疏贝叶斯正则化的阵列SAR高分辨三维成像算法[J]. 雷达学报, 2018, 7(6): 705-716. doi: 10.12000/JR18067
Yan Min, Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. LASAR High-resolution 3D Imaging Algorithm Based on Sparse Bayesian Regularization[J]. Journal of Radars, 2018, 7(6): 705-716. doi: 10.12000/JR18067
Citation: Yan Min, Wei Shunjun, Tian Bokun, Zhang Xiaoling, Shi Jun. LASAR High-resolution 3D Imaging Algorithm Based on Sparse Bayesian Regularization[J]. Journal of Radars, 2018, 7(6): 705-716. doi: 10.12000/JR18067

基于稀疏贝叶斯正则化的阵列SAR高分辨三维成像算法

doi: 10.12000/JR18067
基金项目: 国家自然科学基金(61501098)、博士后面上基金(2015M570778)、高分青年基金(GFZX04061502)
详细信息
    作者简介:

    田博坤(1993–),男,河北沧州人,电子科技大学博士生,主要从事合成孔径雷达成像研究。E-mail: 3544348143@qq.com

    通讯作者:

    韦顺军   weishunjun@uestc.edu.cn

  • 中图分类号: TN957.5

LASAR High-resolution 3D Imaging Algorithm Based on Sparse Bayesian Regularization

Funds: The National Natural Science Foundation of China (61501098), The China Postdoctoral Science Foundation (2015M570778), the High Resolution Earth Observation Youth Foundation (GFZX04061502)
  • 摘要: 阵列合成孔径雷达(Linear Array Synthetic Aperture Radar, LASAR) 3维成像技术是一种具有重要潜在应用价值的雷达成像新体制,但受线阵天线及平台尺寸限制,传统匹配滤波成像算法难以实现LASAR高分辨3维成像。该文利用LASAR回波信号及观测目标的先验分布特性,提出了一种基于快速稀疏贝叶斯正则化重构的LASAR高分辨3维成像算法。该算法先结合贝叶斯估计准则及最大似然估计原理,构造LASAR目标重构的稀疏贝叶斯最小化代价函数;再利用迭代正则化方法求解联合范数最优化问题实现LASAR稀疏目标高分辨3维成像。另外,针对稀疏贝叶斯正则化成像运算量大的问题,结合位置预测快速成像思路,利用阈值分割算法对稀疏粗成像进行强目标提取,进而提升算法运算效率。仿真数据和实测数据验证了该文算法的有效性。

     

  • 图  1  LASAR正下视3维成像的几何模型

    Figure  1.  The geographic model of down-looking LASAR imaging

    图  2  快速SBR算法流程图

    Figure  2.  Fast SBR algorithm flow chart

    图  3  原始仿真场景

    Figure  3.  The simulated model

    图  4  点目标场景成像结果(左:全部数据;中:50%随机抽取数据;右:20%随机抽取数据)

    Figure  4.  The imaging results of the point targets scene (Left: all data; Middle: 50% randomly selected data; Right: 20% randomly selected data)

    图  5  飞机模型场景成像结果(左:全部数据;中:50%随机抽取数据;右:20%随机抽取数据)

    Figure  5.  The imaging results of the airplane model (Left: all data; Middle: 50% randomly selected data; Right: 20% randomly selected data)

    图  6  飞机模型成像评价结果

    Figure  6.  Airplane model imaging evaluation results

    图  7  地基等效LASAR成像实验

    Figure  7.  The ground-based LASAR experiment

    图  8  地基LASAR合成阵列平面及回波数据

    Figure  8.  The virtual array antenna and the echo of the ground-based LASAR

    图  9  实测数据成像结果(左:全部数据;中:50%随机抽取数据;右:20%随机抽取数据)

    Figure  9.  The imaging results of experimental data (Left: all data; Middle: 50% randomly selected data; Right: 20% randomly selected data)

    图  10  实测数据成像评价结果

    Figure  10.  Experimental data imaging evaluation results

    表  1  SBR算法

    Table  1.   SBR algorithm

     算法:SBR算法
     输入:测量信号 ${{{y}}_S}$,测量矩阵 ${{Θ}}$,迭代误差门限 $\gamma $
     输出:稀疏散射系数 $\hat {{α}}$
     初始化:估计值 ${\hat {{α}}^{\left( 0 \right)}} = {{{Θ}}^{\rm H}}{{{y}}_S}$, ${\hat \beta ^{\left( 0 \right)}} = {{\left\| {{{{y}}_S} - {{Θ}}{{\hat {{α}}}^{\left( 0 \right)}}} \right\|_2^2}\Bigr/N}$,
    ${{\hat \varepsilon } ^{\left( 0 \right)}} = {{\left\| {{{\hat {{α}}}^{\left( 0 \right)}}} \right\|_2^2}\Bigr/M}$,迭代次数 $n = 0$
     循环开始
     (1) 估计散射系数向量 ${\hat {{α}}^{(n)}}$:
      ${\hat {{α}}^{\left( n \right)}} = {\left( {{{{{Θ}}}^{\rm H}}{{{Θ}}} + {\beta ^{\left( {n - 1} \right)}}\Bigr/{\varepsilon ^{\left( {n - 1} \right)}}} \right)^{ - 1}}{{{{Θ}}}^{\rm H}}{{{y}}_S}$
     (2) 估计噪声方差 ${\hat\beta ^{\left( n \right)}}$:
      ${\hat\beta ^{\left( n \right)}} = {{\left\| {{{{y}}_S} - {{Θ}}{{\hat {{α}}}^{(n)}}} \right\|_2^2}\Bigr/N}$
     (3) 估计参数 ${\hat\varepsilon ^{\left( n \right)}}$:
      ${\hat\varepsilon ^{\left( n \right)}} = {{\left\| {{{\hat {{α}}}^{\left( n \right)}}} \right\|_2^2}\Bigr/M}$
     (4) 迭代判定:若 ${{\left\| {{{{α}}^{\left( n \right)}} - {{{α}}^{\left( {n - 1} \right)}}} \right\|_2^{}}\Bigr/{\left\| {{{{α}}^{\left( n \right)}}} \right\|_2^{}}} \ge \gamma $且 $n < {I_{\rm iter}}$,则
      $n \leftarrow n + 1$,执行(1)—(4);否则,结束循环。
     循环结束
     结果: $\hat {{α}} \leftarrow {{{α}}^{\left( n \right)}}$
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-08-31
  • 修回日期:  2018-12-15
  • 刊出日期:  2018-12-28

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