基于自学习稀疏先验的三维SAR成像方法

王谋 韦顺军 沈蓉 周梓晨 师君 张晓玲

王谋, 韦顺军, 沈蓉, 等. 基于自学习稀疏先验的三维SAR成像方法[J]. 雷达学报, 2023, 12(1): 36–52. doi: 10.12000/JR22101
引用本文: 王谋, 韦顺军, 沈蓉, 等. 基于自学习稀疏先验的三维SAR成像方法[J]. 雷达学报, 2023, 12(1): 36–52. doi: 10.12000/JR22101
WANG Mou, WEI Shunjun, SHEN Rong, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12(1): 36–52. doi: 10.12000/JR22101
Citation: WANG Mou, WEI Shunjun, SHEN Rong, et al. 3D SAR imaging method based on learned sparse prior[J]. Journal of Radars, 2023, 12(1): 36–52. doi: 10.12000/JR22101

基于自学习稀疏先验的三维SAR成像方法

doi: 10.12000/JR22101
基金项目: 国家自然科学基金(61671113, 61501098),国家重点研发计划项目(2017-YFB0502700),国家留学基金(202106070063),高分对地观测青年基金(GFZX04061502)
详细信息
    作者简介:

    王 谋,博士生,主要研究方向为雷达信号处理、压缩感知、机器学习等

    韦顺军,博士,副教授,主要研究方向为雷达信号处理、压缩感知、SAR成像系统、SAR图像智能解译等

    沈 蓉,硕士生,主要研究方向为三维SAR成像

    周梓晨,硕士生,主要研究方向为三维SAR成像、雷达信号处理

    师 君,博士,副教授,主要研究方向为雷达信号处理、SAR成像系统、SAR图像智能解译等

    张晓玲,博士,教授,主要研究方向为雷达信号处理、SAR成像系统、SAR图像智能解译等

    通讯作者:

    韦顺军 weishunjun@uestc.edu.cn

  • 责任主编:仇晓兰 Corresponding Editor: QIU Xiaolan
  • 中图分类号: TN957.52

3D SAR Imaging Method Based on Learned Sparse Prior

Funds: The National Natural Science Foundation of China (61671113, 61501098), The National Key Research and Development Program of China (2017-YFB0502700), The China Scholarship Council (202106070063), The High-Resolution Earth Observation Youth Foundation (GFZX04061502)
More Information
  • 摘要: 合成孔径雷达三维成像技术(3D SAR)能通过孔径维度扩展实现三维成像能力,但数据维度大、系统实现难、成像分辨率低。压缩感知稀疏重构技术在简化3D SAR系统、提升成像质量等方面展现出巨大潜力,但面临计算复杂度高、参数设置困难、弱稀疏场景适应差等新问题,制约了其实际应用。针对上述问题,该文结合卷积神经网络的特征学习及迭代算法的深度展开理论,提出了基于自学习稀疏先验的3D SAR成像方法。首先,探讨了常规3D SAR稀疏成像中矩阵向量线性表征模型的局限性,引入成像算子提升成像算法处理效率。其次,讨论了迭代算法映射网络的深度展开模型和实现方式,包括网络拓扑结构设计、算法参数的优化约束及网络的训练方法。最后,通过仿真数据和地面实验,证明了所提方法在提升成像精度的同时,其运行时间较传统稀疏成像算法降低一个数量级。

     

  • 图  1  三维SAR成像几何模型

    Figure  1.  3D SAR imaging geometry

    图  2  所提LSISTA网络结构示意图

    Figure  2.  The structure of the proposed LSISTA

    图  3  各算法在采样率分别为70%和30%时的重构结果

    Figure  3.  Reconstructions of different algorithms in cases of sampling rate being 70% and 30%, respectively

    图  4  各算法在SNR分别为10 dB和0 dB时的重构结果

    Figure  4.  Reconstructions of different algorithms in cases of SNR being 10 dB and 0 dB, respectively

    图  5  F16三维模型

    Figure  5.  3D F16 model

    图  6  各算法在采样率为50%和30%时的三维仿真成像结果

    Figure  6.  3D imaging results of different algorithms in cases of sampling rate being 50% and 30%

    图  7  各算法在信噪比分别为10 dB和0 dB时的三维仿真成像结果

    Figure  7.  3D imaging results of different algorithms in cases of SNR being 10 dB and 0 dB

    图  8  毫米波三维SAR验证平台及实验场景

    Figure  8.  3D mmW SAR imaging system and experimental scenarios

    图  9  各算法在采样率为50%和30%时钢片测试板的三维成像结果

    Figure  9.  3D imaging results of the testing steel chip by different algorithms when sampling rate are 50% and 30%

    图  10  各算法在采样率为50%和30%时隐匿刀具的成像结果

    Figure  10.  Imaging results of conceal knives by different algorithms when sampling rate are 50% and 30%

    图  11  各稀疏成像算法在不同目标点数时的成像结果

    Figure  11.  Imaging results of different algorithms in cases of the different number of scatterers

    算法1 基于核函数的ISTA稀疏成像算法
    Alg. 1 ISTA sparse imaging algorithm based on
    kernel functions
     输入:稀疏降采样回波E,相位传播矩阵P,迭代步长$\tau $,迭代
        层数T
     输出:稀疏成像结果$ {{\boldsymbol{X}}^{\left( T \right)}} $
     初始化:$t = 1$, ${{\boldsymbol{X}}^{\left( 0 \right)}} = {\mathcal{M}^{\text{H}}}\left( {{\boldsymbol{E}},{{\bar {\boldsymbol{P}}}}} \right)$;
     循环开始
     (1) 更新迭代残差:$ {{\boldsymbol{V}}^{\left( t \right)}} = {\boldsymbol{E}} - \mathcal{M}\left( {{{\boldsymbol{X}}^{\left( {t - 1} \right)}},{\boldsymbol{\bar P}}} \right) $;
     (2) 梯度下降粗估计:$ {{\boldsymbol{R}}^{\left( t \right)}} = {{\boldsymbol{X}}^{\left( {t - 1} \right)}} + \tau {\mathcal{M}^{\text{H}}}\left( {{{\boldsymbol{V}}^{\left( t \right)}},{\boldsymbol{P}}} \right) $;
     (3) 软阈值收缩去噪: ${ {\boldsymbol{X} }^{\left( t \right)} } = {{\rm{soft}}} \left( { { {\boldsymbol{R} }^{\left( t \right)} },\lambda } \right)$, $t = t + 1$;
     (4) 迭代判定:若$t \le T$,则重复步骤(1)—步骤(4);否则,结束循环。
     循环结束
    下载: 导出CSV
    算法2 LSISTA网络稀疏成像算法
    Alg. 2 LSISTA network-based sparse imaging algorithm
     输入:稀疏降采样回波E,相位传播矩阵P
     输出:稀疏成像结果$ {{\boldsymbol{X}}^{\left( T \right)}} $
     初始化:加载卷积核预训练权重,$ \left\{ {{w_1},{b_1},{w_2},{b_2}} \right\} $;
     循环开始
     (1) 根据式(14),由$ \left\{ {{w_1},{b_1},{w_2},{b_2}} \right\} $计算$ {\tau ^{\left( t \right)}} $和$ {\lambda ^{\left( t \right)}} $;
     (2) 更新迭代残差:$ {{\boldsymbol{V}}^{\left( t \right)}} = {\boldsymbol{E}} - \mathcal{M}\left( {{{\boldsymbol{X}}^{\left( {t - 1} \right)}},{\boldsymbol{\bar P}}} \right) $;
     (3) 梯度下降粗估计:$ {{\boldsymbol{R}}^{\left( t \right)}} = {{\boldsymbol{X}}^{\left( {t - 1} \right)}} + {\tau ^{\left( t \right)}}{\mathcal{M}^{\text{H}}}\left( {{{\boldsymbol{V}}^{\left( t \right)}},{\boldsymbol{P}}} \right) $;
     (4) 软阈值收缩去噪:
       ${ {\boldsymbol{X} }^{\left( t \right)} } = \tilde {\mathcal{T} }\left( {{\rm{soft}}} \left( {\mathcal{T}\left( { { {\boldsymbol{R} }^{\left( t \right)} } } \right),{\lambda ^{\left( t \right)} } } \right) \right)$, $t = t + 1$;
     (5) 迭代判定:若$t \le T$,则重复步骤(1)—步骤(5);否则,结束循环。
     循环结束
    下载: 导出CSV

    表  1  各算法在不同采样率情况下的MAE值

    Table  1.   MAEs of different algorithms in cases of sampling rate being 70% and 30%, respectively

    算法Profile #1Profile #2Profile #3
    70%30%70%30%70%30%
    RMA0.0640.1040.0750.1180.0630.105
    ISTA0.0240.0380.0370.0580.0250.041
    RMIST-Net0.0140.0220.0230.0340.0150.024
    LSISTA0.0050.0120.0060.0170.0050.013
    下载: 导出CSV

    表  2  各算法在不同SNR情况下的MAE值

    Table  2.   MAEs of different algorithms in cases of SNR being 10 dB and 0 dB, respectively

    算法Profile #1Profile #2Profile #3
    10 dB0 dB10 dB0 dB10 dB0 dB
    RMA0.0890.1180.1020.1350.0850.118
    ISTA0.0380.0710.0560.0930.0380.072
    RMIST-Net0.0180.0260.0300.0460.0200.029
    LSISTA0.0050.0080.0060.0150.0050.009
    下载: 导出CSV

    表  3  仿真和实测系统参数

    Table  3.   Parameters in simulations and real experiments

    参数三维SAR仿真值实测系统值
    载频(GHz)7778.8
    带宽(GHz)43.6
    孔径尺寸(cm)100×10040×40
    采样间隔(mm)x : 7.8; z : 7.8x : 1; z : 2
    距离(m)15具体指定
    下载: 导出CSV

    表  4  三维SAR成像仿真在不同降采样率下各算法的图像熵评估

    Table  4.   Image entropy of different algorithms with different sampling rates in simulated 3D SAR imaging

    算法50%30%Time (s) (CPU/GPU)
    RMA2.7573.1280.336/—
    ISTA0.3630.38713.561/—
    RMIST-Net0.0870.0621.522/0.026
    LSISTA0.2990.2897.054/0.033
    下载: 导出CSV

    表  5  三维SAR成像仿真在信噪比下各算法的图像熵评估

    Table  5.   Image entropy of different algorithms with different SNRs in simulated 3D SAR imaging

    算法10 dB0 dBTime (s) (CPU/GPU)
    RMA2.7893.1550.337/—
    ISTA0.5480.88414.523/—
    RMIST-Net0.3770.6321.630/0.030
    LSISTA0.3790.4196.931/0.036
    下载: 导出CSV

    表  6  图9成像实验中各算法的图像熵评估

    Table  6.   Image entropy of different algorithms in the experiment of Fig. 9

    算法50%30%Time (s) (CPU/GPU)
    RMA4.5304.7610.176/—
    ISTA1.1250.9486.604/—
    RMIST-Net0.7490.5840.156/0.010
    LSISTA0.5860.4293.461/0.013
    下载: 导出CSV

    表  7  图10成像实验中各算法的图像熵评估

    Table  7.   Image entropy of different algorithms in the experiment of Fig. 10

    算法50%30%Time (s) (CPU/GPU)
    RMA6.1415.9600.135/—
    ISTA3.5673.0586.086/—
    RMIST-Net2.7192.1590.115/ 0.009
    LSISTA2.3761.8453.103/0.013
    下载: 导出CSV

    表  8  各算法计算复杂度

    Table  8.   Computational complexity of different algorithms

    算法FLOPs
    RMA${N_y}{N_s}\left( {10{{\log }_2}{N_s} + 12} \right)$
    ISTA${N_{{\rm{iter}}} }{N_y}{N_s}\left( {10{ {\log }_2}{N_s} + 12} \right)$
    RMIST-Net$ T{N_y}{N_s}\left( {10{{\log }_2}{N_s} + 12} \right) $
    LSISTA$ T{N_s}\left( {{N_y}\left( {10{{\log }_2}{N_s} + 12} \right) + 2846} \right) $
    下载: 导出CSV

    表  9  各算法在不同目标点数时的MAE评估值

    Table  9.   MAEs in cases of the different number of target points

    目标点数ISTARMIST-NetLSISTA
    99422(37.9%)0.1250.1210.094
    24896(9.5%)0.0270.0260.022
    6203(2.4%)0.0050.0050.003
    下载: 导出CSV
  • [1] 杨建宇. 雷达对地成像技术多向演化趋势与规律分析[J]. 雷达学报, 2019, 8(6): 669–692. doi: 10.12000/JR19099

    YANG Jianyu. Multi-directional evolution trend and law analysis of radar ground imaging technology[J]. Journal of Radars, 2019, 8(6): 669–692. doi: 10.12000/JR19099
    [2] 吴一戎, 朱敏慧. 合成孔径雷达技术的发展现状与趋势[J]. 遥感技术与应用, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012

    WU Yirong and ZHU Minhui. The developing status and trends of synthetic aperture radar[J]. Remote Sensing Technology and Application, 2000, 15(2): 121–123. doi: 10.3969/j.issn.1004-0323.2000.02.012
    [3] CURLANDER J C and MCDONOUGH R N. Synthetic Aperture Radar[M]. New York: Wiley, 1991.
    [4] DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306. doi: 10.1109/TIT.2006.871582
    [5] SHI Wuzhen, JIANG Feng, LIU Shaohui, et al. Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 2019, 29: 375–388. doi: 10.1109/TIP.2019.2928136
    [6] YANG Xianjun, TAO Xiaofeng, DUTKIEWICZ E, et al. Energy-efficient distributed data storage for wireless sensor networks based on compressed sensing and network coding[J]. IEEE Transactions on Wireless Communications, 2013, 12(10): 5087–5099. doi: 10.1109/TWC.2013.090313.121804
    [7] POTTER L C, ERTIN E, PARKER J T, et al. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006–1020. doi: 10.1109/JPROC.2009.2037526
    [8] CAMLICA S, GURBUZ A C, and ARIKAN O. Autofocused spotlight SAR image reconstruction of off-grid sparse scenes[J]. IEEE Transactions on Aerospace and Electronic Systems, 2017, 53(4): 1880–1892. doi: 10.1109/TAES.2017.2675138
    [9] PU Wei and WU Junjie. OSRanP: A novel way for radar imaging utilizing joint sparsity and low-rankness[J]. IEEE Transactions on Computational Imaging, 2020, 6: 868–882. doi: 10.1109/TCI.2020.2993170
    [10] DONOHO D L, MALEKI A, and MONTANARI A. Message-passing algorithms for compressed sensing[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009, 106(45): 18914–18919. doi: 10.1073/pnas.0909892106
    [11] BOYD S, PARIKH N, CHU E, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends® in Machine Learning, 2011, 3(1): 1–122. doi: 10.1561/2200000016
    [12] BECK A and TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems[J]. SIAM Journal on Imaging Sciences, 2009, 2(1): 183–202. doi: 10.1137/080716542
    [13] ZHAO Lifan, WANG Lu, YANG Lei, et al. The race to improve radar imagery: An overview of recent progress in statistical sparsity-based techniques[J]. IEEE Signal Processing Magazine, 2016, 33(6): 85–102. doi: 10.1109/MSP.2016.2573847
    [14] FANG Jian, XU Zongben, ZHANG Bingchen, et al. Fast compressed sensing SAR imaging based on approximated observation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(1): 352–363. doi: 10.1109/JSTARS.2013.2263309
    [15] JIANG Chenglong, ZHANG Bingchen, FANG Jian, et al. Efficient ℓq regularisation algorithm with range–azimuth decoupled for SAR imaging[J]. Electronics Letters, 2014, 50(3): 204–205. doi: 10.1049/el.2013.1989
    [16] GREGOR K and LECUN Y. Learning fast approximations of sparse coding[C]. The 27th International Conference on International Conference on Machine Learning, Haifa, Israel, 2010: 399–406.
    [17] PU Wei. SAE-Net: A deep neural network for SAR autofocus[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5220714. doi: 10.1109/TGRS.2021.3139914
    [18] WANG Mou, WEI Shunjun, SHI Jun, et al. CSR-Net: A novel complex-valued network for fast and precise 3-D microwave sparse reconstruction[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 4476–4492. doi: 10.1109/JSTARS.2020.3014696
    [19] HU Xiaowei, XU Feng, GUO Yiduo, et al. MDLI-Net: Model-driven learning imaging network for high-resolution microwave imaging with large rotating angle and sparse sampling[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5212617. doi: 10.1109/TGRS.2021.3110579
    [20] WEI Yangkai, LI Yinchuan, DING Zegang, et al. SAR parametric super-resolution image reconstruction methods based on ADMM and deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(12): 10197–10212. doi: 10.1109/TGRS.2021.3052793
    [21] WEI Shunjun, LIANG Jiadian, WANG Mou, et al. AF-AMPNet: A deep learning approach for sparse aperture ISAR imaging and autofocusing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5206514. doi: 10.1109/TGRS.2021.3073123
    [22] WANG Mou, WEI Shunjun, ZHOU Zichen, et al. Efficient ADMM framework based on functional measurement model for mmW 3-D SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5226417. doi: 10.1109/TGRS.2022.3165541
    [23] WANG Mou, WEI Shunjun, LIANG Jiadian, et al. TPSSI-Net: Fast and enhanced two-path iterative network for 3D SAR sparse imaging[J]. IEEE Transactions on Image Processing, 2021, 30: 7317–7332. doi: 10.1109/TIP.2021.3104168
    [24] PU Wei. Deep SAR imaging and motion compensation[J]. IEEE Transactions on Image Processing, 2021, 30: 2232–2247. doi: 10.1109/TIP.2021.3051484
    [25] WANG Mou, WEI Shunjun, LIANG Jiadian, et al. Lightweight FISTA-inspired sparse reconstruction network for mmW 3-D holography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5211620. doi: 10.1109/TGRS.2021.3093307
    [26] WANG Mou, WEI Shunjun, LIANG Jiadian, et al. RMIST-Net: Joint range migration and sparse reconstruction network for 3-D mmW imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60: 5205117. doi: 10.1109/TGRS.2021.3068405
    [27] ZHANG Jian and GHANEM B. ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing[C]. The 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1828–1837.
    [28] ZHOU Yulong, ZHONG Yu, WEI Zhun, et al. An improved deep learning scheme for solving 2-D and 3-D inverse scattering problems[J]. IEEE Transactions on Antennas and Propagation, 2021, 69(5): 2853–2863. doi: 10.1109/TAP.2020.3027898
    [29] LOPEZ-SANCHEZ J M and FORTUNY-GUASCH J. 3-D radar imaging using range migration techniques[J]. IEEE Transactions on Antennas and Propagation, 2000, 48(5): 728–737. doi: 10.1109/8.855491
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出版历程
  • 收稿日期:  2022-05-24
  • 修回日期:  2022-07-09
  • 网络出版日期:  2022-07-25
  • 刊出日期:  2023-02-28

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