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多任务协同优化学习高分辨SAR稀疏自聚焦成像算法

杨磊 张苏 黄博 盖明慧 李埔丞

姚志强, 李广龙, 王仕果, 游志宏. 基于Golay互补序列的压缩感知稀疏信道估计算法[J]. 电子与信息学报, 2016, 38(2): 282-287. doi: 10.11999/JEIT150594
引用本文: 杨磊, 张苏, 黄博, 盖明慧, 李埔丞. 多任务协同优化学习高分辨SAR稀疏自聚焦成像算法[J]. 电子与信息学报, 2021, 43(9): 2711-2719. doi: 10.11999/JEIT200300
YAO Zhiqiang, LI Guanglong, WANG Shiguo, YOU Zhihong. Compressed Sensing Channel Estimation Algorithm Based on Deterministic Sensing with Golay Complementary Sequences[J]. Journal of Electronics & Information Technology, 2016, 38(2): 282-287. doi: 10.11999/JEIT150594
Citation: Lei YANG, Su ZHANG, Bo HUANG, Minghui GAI, Pucheng LI. Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery[J]. Journal of Electronics & Information Technology, 2021, 43(9): 2711-2719. doi: 10.11999/JEIT200300

多任务协同优化学习高分辨SAR稀疏自聚焦成像算法

doi: 10.11999/JEIT200300
基金项目: 国家自然科学基金(61601470),天津市自然科学基金(16JCYBJC41200),预研基金(61406190101)
详细信息
    作者简介:

    杨磊:男,1984年生,副教授,研究方向为高分辨SAR成像及机器学习理论应用

    张苏:女,1996年生,硕士生,研究方向为高分辨SAR成像及优化学习理论

    黄博:男,1986年生,博士生,研究方向为雷达高度表系统及信号处理

    盖明慧:女,1997年生,硕士生,研究方向为高分辨SAR成像及优化学习理论

    李埔丞:男,1992年生,博士生,研究方向为高分辨SAR成像及优化学习理论

    通讯作者:

    杨 磊 yanglei840626@163.com

  • 中图分类号: TN957.52

Multi-task Learning of Sparse Autofocusing for High-Resolution SAR Imagery

Funds: The National Natural Science Foundation of China(61601470), The Natural Science Foundation of Tianjin, China (16JCYBJC41200), The Equipment Pre-research Fund(61406190101)
  • 摘要: 针对传统高分辨合成孔径雷达(SAR)稀疏自聚焦成像算法难以有效平衡稀疏与聚焦特征的问题,该文提出一种基于交替方向多乘子方法(ADMM)的多任务协同优化学习稀疏自聚焦(MtL-SA)算法。该算法通过引入熵范数表征SAR成像结果聚焦特征,在ADMM优化框架下,利用近端算法求解聚焦特征解析解。针对原熵范数正则优化目标函数的非凸问题,该文合理设计代价函数,从而保证熵范数近端算子的闭合解析解。同时,应用1范数表征成像结果稀疏特征,并建立面向复数SAR成像数据的复数软阈值近端算子。该文所提MtL-SA成像算法可实现对目标场景后向散射场对应稀疏特征和聚焦特征的解析求解,并有效提升自聚焦算法的可靠性和稳健性。两种特征增强处理相互调和,保证了算法运行过程中有效降低误差传播,进而保证联合特征增强精度。仿真及实测机载SAR成像数据实验,验证了算法的有效性和实用性,同时应用相变分析方法分别定量和定性地分析了该文所提算法相比其他传统算法的优越性。
  • 图  1  SAR几何模型示意图

    图  2  仿真实验结果

    图  3  实测数据实验结果

    图  4  2维等高线对比图

    图  5  信噪比-降采样率相变热力图

    表  1  MtL-SA算法流程

     步骤1  设定初值X0=Z0=D0=0,k=0,G=2,设定迭代停止准则,开始循环。
     步骤2  全局优化:X更新运算Xk+1=[AHEHY+ρ1(Z1k+D1k)+ρ2(Z2k+D2k)](AHA+ρGI)1
     步骤3  局部优化:Z1, D1, Z2D2顺次更新运算
     forg=G
     Zk+11=(Ek+1)HAHY,Dk+11=Dk1Xk+1+Zk+11
     Zk+12=proxλ2/ρ[Wk+1],Dk+12=Dk2Xk+1+Zk+12
     end
     Zk+1=[Zk+11Zk+12],Dk+1=[Dk+11Dk+12],k=k+1
     步骤4 当残差小于加性噪声方差时,跳至步骤5,迭代结束。否则,跳至步骤2。
     步骤5  输出联合稀疏聚焦特征增强后的图像数据X
    下载: 导出CSV
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    其他类型引用(13)

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出版历程
  • 收稿日期:  2020-04-24
  • 修回日期:  2021-02-28
  • 网络出版日期:  2021-03-22
  • 刊出日期:  2021-09-16

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