一种基于元学习的稀疏孔径ISAR成像算法

夏靖远 杨志雄 周治兴 廖淮璋 张双辉 付耀文

夏靖远, 杨志雄, 周治兴, 等. 一种基于元学习的稀疏孔径ISAR成像算法[J]. 雷达学报, 2023, 12(4): 849–859. doi: 10.12000/JR23121
引用本文: 夏靖远, 杨志雄, 周治兴, 等. 一种基于元学习的稀疏孔径ISAR成像算法[J]. 雷达学报, 2023, 12(4): 849–859. doi: 10.12000/JR23121
XIA Jingyuan, YANG Zhixiong, ZHOU Zhixing, et al. A metalearning-based sparse aperture ISAR imaging method[J]. Journal of Radars, 2023, 12(4): 849–859. doi: 10.12000/JR23121
Citation: XIA Jingyuan, YANG Zhixiong, ZHOU Zhixing, et al. A metalearning-based sparse aperture ISAR imaging method[J]. Journal of Radars, 2023, 12(4): 849–859. doi: 10.12000/JR23121

一种基于元学习的稀疏孔径ISAR成像算法

doi: 10.12000/JR23121
基金项目: 国家自然科学基金(62171448, 61921001, 62131020, 62022091),湖南省杰出青年基金(2022JJ10067)
详细信息
    作者简介:

    夏靖远,博士,讲师,研究方向为非凸优化、机器学习、表征学习

    杨志雄,博士生,研究方向为图像处理、信号处理技术

    周治兴,硕士生,研究方向为图像处理、信号处理技术

    廖淮璋,博士生,研究方向为图像生成、多模态数据融合

    张双辉,博士,副研究员,硕士生导师,研究方向为雷达成像、压缩感知、贝叶斯推断

    付耀文,博士,研究员,博士生导师,研究方向为雷达信号处理、信息融合技术

    通讯作者:

    杨志雄 yzx21@nudt.edu.cn

    张双辉 zhangshuanghui@nudt.edu.cn

  • 责任主编:张磊 Corresponding Editor: ZHANG Lei
  • 中图分类号: TN957.51

A Metalearning-based Sparse Aperture ISAR Imaging Method

Funds: The National Natural Science Foundation of China (62171448, 61921001, 62131020, 62022091), Distinguished Youth Science Foundation of Hunan Province (2022JJ10067)
More Information
  • 摘要: 稀疏孔径逆合成孔径雷达(ISAR)成像的目标是从不完整的回波中恢复和重建高质量ISAR图像,现有方法主要可以分为基于模型的方法和基于深度学习的方法两大类:一方面,基于模型的稀疏孔径ISAR成像方法往往具备显性的数学模型,对雷达回波的成像过程有清晰的物理建模,但算法有效性上不如基于学习的方法。另一方面,基于深度学习的方法通常高度依赖训练数据,难以适配空间目标ISAR成像任务中高实时、高动态的现实应用需求。针对上述问题,该文提出了一种基于元学习的高效、自适应稀疏孔径ISAR成像算法。所提方法主要包含基于学习辅助的交替迭代优化和元学习优化两部分。基于学习辅助的交替迭代优化继承了ISAR成像机理的回波成像模型,保证了方法数学物理可解释性的同时避免了方法对数据的依赖性;基于元学习的优化策略通过引入非贪婪优化策略,提高了算法跳出局部最优解的能力,保证了病态非凸条件下的算法收敛性能。最后,实验结果表明:该文方法可以在不依赖训练数据、不进行预训练的情况下实现高效、自适应的稀疏孔径ISAR成像,并取得优于其他常规ISAR成像算法的性能。

     

  • 图  1  ISAR雷达观测模型

    Figure  1.  General ISAR imaging scenario

    图  2  本文方法网络结构示意图

    Figure  2.  The network architecture of the proposed method

    图  3  不同ISAR成像方法在仿真数据上的可视化对比结果

    Figure  3.  The visual imaging results on the of the simulated ISAR data

    图  4  不同ISAR成像方法在实测数据上的可视化对比结果(稀疏率为0.25)

    Figure  4.  The visual imaging results on the of the real ISAR data (sparsity rate 0.25)

    图  5  不同ISAR成像方法在实测数据上的可视化对比结果(稀疏率为0.125)

    Figure  5.  The visual imaging results on the of the real ISAR data (sparsity rate 0.125)

    图  6  消融实验成像结果

    Figure  6.  The visual results of the ablation studies of the proposed method

    1  一种基于元学习的稀疏孔径ISAR成像算法

    1.   A meta-learning based sparse aperture ISAR imaging method

     1 给定:稀疏孔径一维距离像S
     2 初始化:网络输入$ {\boldsymbol{Z}}_{\boldsymbol{X}}^{\mathrm{0,0}} $,网络参数$ {\boldsymbol{\theta }}_{\boldsymbol{X}}^{\mathrm{0,0}} $。
     3 for $ k\leftarrow \mathrm{0,1},\cdots ,K $ do
     4   for $ t\leftarrow \mathrm{0,1},\cdots ,T $ do
     5    ${\boldsymbol{X} }^{(k,t)}={ {{G} } }_{\boldsymbol{X} }\left({\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t)},{\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(k\right)}\right)$
     6    ${\mathcal{L} }_{ {\boldsymbol{Z} }_{\boldsymbol{X} } }^{(k,t)}={\left\|\boldsymbol{S}-\boldsymbol{D}\boldsymbol{F}{\boldsymbol{X} }^{(k,t)}\right\|}_{{\rm{F}}}^{2}+\beta {\left\|{\boldsymbol{X} }^{(k,t)}\right\|}_{1}$
     7    ${\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t+1)}={\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t)}-{\gamma }_{\boldsymbol{X} }^{(k,t)}\cdot {\rm{Adam}}\left({\nabla }_{ {\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t)} }{\mathcal{L} }_{ {\boldsymbol{Z} }_{\boldsymbol{X} } }^{(k,t)}\right)$
     8   end
     9  ${\mathcal{L} }_{ {\rm{meta} } }^{\left(k\right)}=\displaystyle\sum _{t=1}^{T}\left\{ {\left\|\boldsymbol{S}-\boldsymbol{D}\boldsymbol{F}\cdot { { {G} } }_{\boldsymbol{X} }\left({\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t)},{\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(k\right)}\right)\right\|}_{{\rm{F}}}^{2}\right.$
           $\left. +\beta {\left\|{ {{G} } }_{\boldsymbol{X} }\left({\boldsymbol{Z} }_{\boldsymbol{X} }^{(k,t)},{\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(k\right)}\right)\right\|}_{1}\right\}$
     10 ${\boldsymbol{\theta } }_{\boldsymbol{X} }^{(k+1)}={\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(k\right)}-{\gamma }_{{\rm{meta}}}^{\left(k\right)}\cdot {\rm{Adam}}\left({\nabla }_{ {\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(k\right)} }{\mathcal{L} }_{{\rm{meta}}}^{\left(k\right)}\right)$
     11 $ {\boldsymbol{Z}}_{\boldsymbol{X}}^{(k+1, 0)}={\boldsymbol{Z}}_{\boldsymbol{X}}^{(k,T)} $
     12 end
     13 输出:${\boldsymbol{X} }^{(K,T)}={ {{G} } }_{\boldsymbol{X} }\left({\boldsymbol{Z} }_{\boldsymbol{X} }^{(K,T)},{\boldsymbol{\theta } }_{\boldsymbol{X} }^{\left(K\right)}\right)$
    下载: 导出CSV

    表  1  不同方法在仿真ISAR数据集上的平均成像性能对比(稀疏率为0.250)

    Table  1.   The average imaging results on the of the simulated ISAR data (sparsity rate 0.250)

    方法图像熵PSNR (dB)RMSE
    RD7.948042.83140.0399
    OMP[4]5.498751.89500.0157
    ADMM[5]6.583847.57090.0279
    CU-ADMM[19]5.392152.17660.0152
    本文方法5.150052.96020.0143
    下载: 导出CSV

    表  2  不同方法在仿真ISAR数据集上的平均成像性能对比(稀疏率为0.125)

    Table  2.   The average imaging results on the of the simulated ISAR data (sparsity rate 0.125)

    方法图像熵PSNR (dB)RMSE
    RD8.147639.30110.0601
    OMP[4]5.291450.75180.0183
    ADMM[5]6.784849.28350.0305
    CU-ADMM[19]5.144350.97060.0160
    本文方法5.114351.24600.0155
    下载: 导出CSV

    表  3  不同方法在实测ISAR数据集上的平均成像性能对比(稀疏率为0.250)

    Table  3.   The average imaging results on the of the real ISAR data (sparsity rate 0.250)

    方法图像熵PSNR (dB)RMSE
    RD8.158042.83140.0399
    OMP[4]7.105645.45460.0206
    ADMM[5]7.324046.52750.0190
    CU-ADMM[19]7.649345.88630.0203
    本文方法6.532146.64570.0186
    下载: 导出CSV

    表  4  不同方法在实测ISAR数据集上的平均成像性能对比(稀疏率为0.125)

    Table  4.   The average imaging results on the of the real ISAR data (sparsity rate 0.125)

    方法图像熵PSNR (dB)RMSE
    RD8.382739.39770.0428
    OMP[4]5.602846.72210.0184
    ADMM[5]4.169046.21110.0195
    CU-ADMM[19]4.163046.33990.0192
    本文方法4.103547.23700.0171
    下载: 导出CSV

    表  5  本文方法中元学习优化的消融实验

    Table  5.   The ablation studies of the proposed method

    稀疏率方法图像熵PSNR (dB)RMSE
    0.250本文方法5.150052.96020.0143
    无元学习模块5.598651.28570.0191
    0.125本文方法5.114351.24600.0174
    无元学习模块5.269750.36750.0206
    下载: 导出CSV

    表  6  5种方法的计算复杂度对比

    Table  6.   The computational complexity comparison of five methods

    方法计算复杂度训练
    时间(h)
    测试
    时间(s)
    RD${\mathcal{O} }\left({ {{M} } }^{2}\right)$$ < $1.0
    OMP[4]${\mathcal{O} }\left({{K} }{ {{M} } }^{2}\right)$4.4
    ADMM[5]${\mathcal{O} }\left({{K} }{ {{M} } }^{3}\right)$1.6
    CU-ADMM[19]${\mathcal{O} }\left({{K} }{ {{M} } }^{3}\right)$2$ < $1.0
    本文方法${\mathcal{O} }\left({{K} }{ {{M} } }^{2}{{N} }{\displaystyle\sum }_{l=1}^{L}{ {{C} } }_{l}{ {{C} } }_{l+1}\right)$18.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-05
  • 修回日期:  2023-08-10
  • 网络出版日期:  2023-08-22
  • 刊出日期:  2023-08-28

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