High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error

QU Haiyou CHENG Di CHEN Chang CHEN Weidong

屈海友, 程迪, 陈畅, 等. 涡旋雷达高分辨率稀疏自校正相位误差成像[J]. 雷达学报, 2021, 10(5): 699–717. DOI: 10.12000/JR21094
引用本文: 屈海友, 程迪, 陈畅, 等. 涡旋雷达高分辨率稀疏自校正相位误差成像[J]. 雷达学报, 2021, 10(5): 699–717. DOI: 10.12000/JR21094
QU Haiyou, CHENG Di, CHEN Chang, et al. High-resolution sparse self-calibration imaging for vortex radar with phase error[J]. Journal of Radars, 2021, 10(5): 699–717. DOI: 10.12000/JR21094
Citation: QU Haiyou, CHENG Di, CHEN Chang, et al. High-resolution sparse self-calibration imaging for vortex radar with phase error[J]. Journal of Radars, 2021, 10(5): 699–717. DOI: 10.12000/JR21094

High-resolution Sparse Self-calibration Imaging for Vortex Radar with Phase Error

doi: 10.12000/JR21094
Funds: The National Natural Science Foundation of China (61971392)
More Information
    Author Bio:

    QU Haiyou received the B.S. degree in electronic engineering from Nanjing University of Science and Technology in 2019. He is currently pursuing the Ph.D. degrees in the School of Information Science and Technology, University of Science and Technology of China, Hefei, China. His research interests include vortex electromagnetic imaging, Bayesian model, optimization algorithms and signal processing

    CHENG Di received B.S. degree in remote sensing science and technology from Harbin Institute of Technology, Harbin, China, in 2018. He is currently pursuing the Ph.D. degrees in the School of Information Science and Technology, University of Science and Technology of China, Hefei, China. His research interests include signal processing, radar imaging, optimization algorithms and target tracking

    CHEN Chang received B.S., M.S. and Ph.D. degrees in University of Science and Technology of China (USTC), Hefei, China, in 2002, 2005 and 2012 respectively. He is an Associate Professor in the School of Information Science and Technology, USTC, China. He has considerable experience in antenna design, microwave imaging, and microwave passive components. He is currently interested in microwave antennas, microwave metamaterials, and electromagnetic spectrum sensing

    CHEN Weidong received the B.S. degree in electronic engineering from the University of Electronic Science and Technology of China, Chengdu, China, in 1990, and the M.Eng. and Ph.D. degrees in electromagnetic field and microwave technology from the University of Science and Technology of China (USTC), Hefei, China, in 1994 and 2005, respectively. He serves as a Professor with the Department of Electronic Engineering, USTC. His research interests are in the areas of microwave imaging theory with applications to radar

    Corresponding author: CHEN Chang, chench@mail.ustc.edu.cn
  • 摘要:

    基于轨道角动量(OAM)的涡旋雷达因其在高分辨率成像方面具有巨大潜力而受到广泛关注。有限OAM模式下的涡旋雷达高分辨率成像问题,通常采用稀疏恢复的方法来解决,这种方法需要精确地已知成像模型的先验知识。然而,系统中不可避免存在的相位误差,会导致成像模型失配,严重影响成像性能。为了解决这一问题,该文首次建立了存在相位误差时的涡旋雷达成像模型。同时,提出了一种涡旋雷达两步自校正成像方法,用于直接估计相位误差。首先在第1步中提出了一种稀疏驱动算法来促进目标稀疏性,同时提升成像重构性能。其次,在第2步中提出了一种直接补偿相位误差的自校正操作。该方法通过对目标重构和相位误差估计的交替迭代,能够很好地重建目标并有效地补偿相位误差。仿真结果表明,该方法在提高成像质量和改善相位误差估计性能方面具有潜在的优势。

     

  • Figure  1.  Vortex radar observation coordinate based on phased UCA

    Figure  2.  The azimuth dimension point spread function

    Figure  3.  In mode number [–10, 10], the obtained vortex EM images with phase error using different algorithms for SNR=20 dB

    Figure  4.  The proposed self-calibration algorithm performance

    Figure  5.  ${\rm{NMSE}}({\hat{\boldsymbol \beta }})$ of phase error under different SNRs and different phase error range

    Figure  6.  In mode number [–10, 10], the obtained vortex EM images of target 1 scene with phase error using different algorithms for SNR=5 dB

    Figure  7.  Under different SNRs, and obtained of original target 1 scene in mode number [–10, 10]

    Figure  8.  In mode number [–10, 10], the obtained vortex EM images of target 2 scene with phase error using different algorithms for SNR=20 dB

    Figure  9.  In mode number [–10, 10], the obtained vortex EM images of target 3 scene with phase error using different algorithms for SNR=20 dB

    Figure  10.  Under different SNRs, ${\rm{NMSE}}({{\boldsymbol{\hat{\sigma}} }})$ and ${\rm{Corr}}({{\boldsymbol{\hat{\sigma }}}},\,{\boldsymbol{\sigma }})$ obtained of original target 2 scene in mode number [–10, 10]

    Figure  11.  Under different SNRs, ${\rm{NMSE}}({{\boldsymbol{\hat{\sigma }}}})$ and ${\rm{Corr}}({\boldsymbol{{\hat{\sigma }}}},\,{\boldsymbol{\sigma }})$ obtained of original target 3 scene in mode number [–10, 10]

     Algorithm 1 Algorithm flow of VSCIP
     Input: $ {{\boldsymbol{y}},{\boldsymbol{S}}},\epsilon,{a}_{0},{b}_{0}$
     Initialization: ${\boldsymbol{\zeta } } = {\boldsymbol{1} },{\boldsymbol{\mu } } = { {\boldsymbol{S} }^{\rm{H}}}{\boldsymbol{y} }$
     While $j < {j_{\max }}$ do
       For $t = 1$ to ${t_{\max }}$ do
        Update ${{\boldsymbol{\mu }}^j}$ and ${{\boldsymbol{\varSigma }}^j}$ by Eqs. (30) and (29);
        Update ${{\boldsymbol{\lambda }}^j}$ by Eq. (39);
        Update $\left\langle {\ln {\pi _m} } \right\rangle$ and $\left\langle {\ln \left( {1 - {\pi _m} } \right)} \right\rangle$ by Eqs. (42) and (43);
        Update ${{\boldsymbol{\zeta }}^j}$ by Eq. (50);
        Update ${\eta ^j}$ by Eq. (46);
        If $\left\| { { {\hat {\boldsymbol{\mu } } }^j}(t + 1) - { {\hat {\boldsymbol{\mu } } }^j}(t)} \right\|_2^2/\left\| {\hat {\boldsymbol{\mu } }{ {(t)}^j} } \right\|_2^2 < \epsilon$, break;
        ${{\boldsymbol{x}}^j} = {{\boldsymbol{\mu }}^j}(t + 1),{{\boldsymbol{w}}^j} = {{\boldsymbol{w}}^j}(t + 1)$;
        ${{\boldsymbol{\sigma }}^j}={{\boldsymbol{x}}^j} \odot {{\boldsymbol{w}}^j}$;
       End For
      Update ${{\boldsymbol{\beta }}^j}$ by Eq. (53);
      Recompute ${\boldsymbol{S}}({{\boldsymbol{\beta }}^j})$;
     End While
     Output: ${{\boldsymbol{\sigma }}^j}$, ${{\boldsymbol{\beta }}^j}$
    下载: 导出CSV

    Table  1.   Key radar parameters for simulations

    Parameters Value
    Frequency of the first subpulse $ f_0 $ 9.9 GHz
    Bandwith $B_r $ 200 MHz
    Number of subpulse $D $ 41
    Topological charge $ \alpha $ [–10, 10]
    Array radius $a $ 0.25 m
    Array elements number N 22
    Target range (985, 1015) m
    Target azimuth (0.2, 0.6)π rad
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-01
  • 修回日期:  2021-08-05
  • 网络出版日期:  2021-08-21
  • 刊出日期:  2021-10-28

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