Volume 9 Issue 1
Feb.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(1): 166-173
PAN Jie, WANG Shuai, LI Daojing, et al. High-resolution Wide-swath SAR moving target imaging technology based on distributed compressed sensing[J]. Journal of Radars, 2020, 9(1): 166–173. doi: 10.12000/JR19060
Citation: PAN Jie, WANG Shuai, LI Daojing, et al. High-resolution Wide-swath SAR moving target imaging technology based on distributed compressed sensing[J]. Journal of Radars, 2020, 9(1): 166–173. doi: 10.12000/JR19060
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High-resolution Wide-swath SAR Moving Target Imaging Technology Based on Distributed Compressed Sensing

doi: 10.12000/JR19060
  • 1.

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • 2.

    National Key Lab of Microwave Imaging Technology, Beijing 100190, China

  • 3.

    University of Chinese Academy of Sciences, Beijing 100049, China

  • 4.

    National Time Service Center, Chinese Academy of Sciences, Xian 710600, China

Funds:  The Key Standard Technologies of National High Resolution Special (30-H30C01-9004-19/21)
More Information
  • Corresponding author: PAN Jie, panjie@aircas.ac.cn
  • Received Date: 2019-06-14
  • Rev Recd Date: 2019-11-20
    • Available Online: 2019-12-04
  • Publish Date: 2020-02-28
    Abstract
  • High-resolution wide-swath SAR moving target imaging is of great significance for target tracking. To achieve target tracking, conversional space-based multichannel SAR technology requires a large number of channels. However, this leads to high system complexity. Moreover, paired false target echoes exist in the azimuth. To address such problems, a high-resolution wide-swath SAR moving target imaging technology based on distributed compressed sensing is proposed in this paper. When the number of channels is large enough, the number of channels is approximately half of that of the conventional high-resolution wide-swath target imaging configuration channel. A distributed compressed sensing observation model is constructed using the sparse property of moving targets and the nonsparse characteristics of the clutter background. Clutter background and sparse moving targets are reconstructed by combining one-dimensional distributed compressed sensing reconstruction in the azimuth and two-dimensional distributed compressed sensing reconstruction in the range-azimuth. Moreover, the paired false target echoes in multichannel SAR moving target are suppressed. The simulation results combined with RADAR-SAT data verify the effectiveness of the proposed technology.

     

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    • References(16)
  • [1]
    FREEMAN A, JOHNSON W T K, HUNEYCUTT B, et al. The "Myth" of the minimum SAR antenna area constraint[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(1): 320–324. doi: 10.1109/36.823926
    [2]
    ZHANG Shuangxi, XING Mengdao, XIA Xianggen, et al. Multichannel HRWS SAR imaging based on range-variant channel calibration and multi-doppler-direction restriction ambiguity suppression[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4306–4327. doi: 10.1109/TGRS.2013.2281329
    [3]
    叶恺, 禹卫东, 徐伟, 等. 基于柱形抛物面天线的MIMO SAR研究[J]. 电子与信息学报, 2018, 40(8): 1816–1822. doi: 10.11999/JEIT171105

    YE Kai, YU Weidong, XU Wei, et al. Investigation on parabolic cylinder reflector based MIMO SAR[J]. Journal of Electronics &Information Technology, 2018, 40(8): 1816–1822. doi: 10.11999/JEIT171105
    [4]
    DING Chibiao, LIANG Xingdong, WANG Jie, et al. An novel airborne MIMO-SAR system built in IECAS[C]. 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, USA, 2017: 2423–2426. doi: 10.1109/IGARSS.2017.8127481.
    [5]
    周建卫, 李道京, 田鹤, 等. 基于共形稀疏阵列的艇载外辐射源雷达性能分析[J]. 电子与信息学报, 2017, 39(5): 1058–1063. doi: 10.11999/JEIT160846

    ZHOU Jianwei, LI Daojing, TIAN He, et al. Performance analysis on airship-borne passive radar based on conformal sparse array[J]. Journal of Electronics &Information Technology, 2017, 39(5): 1058–1063. doi: 10.11999/JEIT160846
    [6]
    KIRK J C, DARDEN S, MAJUMDER U, et al. Forty years of digital SAR and slow GMTI technology[C]. 2014 IEEE Radar Conference, Cincinnati, USA, 2014: 64–69. doi: 10.1109/RADAR.2014.6875556.
    [7]
    HENDERSON F M and LEWIS A J. Principles and Applications of Imaging Radar[M]. New York: Wiley, 1998. 89–90.
    [8]
    GIERULL C H. Statistical analysis of multilook SAR interferograms for CFAR detection of ground moving targets[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 691–701. doi: 10.1109/TGRS.2003.821886
    [9]
    GEBERT N. Multi-channel azimuth processing for high-resolution wide-swath SAR imaging[D]. [Ph.D. dissertation], German Aerospace Center, 2009.
    [10]
    BAUMGARTNER S V and KRIEGER G. Simultaneous high-resolution wide-swath SAR imaging and ground moving target indication: Processing approaches and system concepts[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(11): 5015–5029. doi: 10.1109/JSTARS.2015.2450019
    [11]
    SUN Guangcai, XING Mengdao, XIA Xianggen, et al. Robust ground moving-target imaging using deramp-keystone processing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 51(2): 966–982. doi: 10.1109/TGRS.2012.2204889
    [12]
    LAZAROV A D and MINCHEV C N. SAR imaging of a moving target[C]. The 2007 3rd International Conference on Recent Advances in Space Technologies, Istanbul, Turkey, 2017: 366–372. doi: 10.1109/RAST.2007.4284014.
    [13]
    ZHANG Shuangxi, XING Mengdao, XIA Xianggen, et al. A robust channel-calibration algorithm for multi-channel in azimuth HRWS SAR imaging based on local maximum-likelihood weighted minimum entropy[J]. IEEE Transactions on Image Processing, 2013, 22(12): 5294–5305. doi: 10.1109/TIP.2013.2274387
    [14]
    ZHANG Yun, GUO Yiran, JI Zhenyuan, et al. A novel algorithm of radial velocity estimation for MC-HRWS system[C]. 2017 International Conference on Communications, Signal Processing, and Systems, Harbin, China, 2019: 1396–1403. doi: 10.1007/978-981-10-6571-2_168.
    [15]
    CANDES E J and TAO T. Near-optimal signal recovery from random projections: Universal encoding strategies?[J]. IEEE Transactions on Information Theory, 2006, 52(12): 5406–5425. doi: 10.1109/TIT.2006.885507
    [16]
    DUARTE M F, SARVOTHAM S, BARON D, et al. Distributed compressed sensing of jointly sparse signals[C]. Conference Record of the Thirty-Ninth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, USA, 2005: 1537–1541. doi: 10.1109/ACSSC.2005.1600024.
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