ISSN 0253-2778

CN 34-1054/N

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Segmented reconstruction for SAR imaging based on 1-bit compressed sensing

  • 1.

    Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China

  • 2.

    Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2017.10.007
  • Received Date: 26 September 2016
  • Rev Recd Date: 30 November 2016
  • Publish Date: 31 October 2017
    Abstract

  • Abstract

    In Synthetic Aperture Radar (SAR) systems, imaging algorithms based on 1-bit have a great advantage because the echo’s SNR is generally not high. However, those algorithms often require more time than the match filter strategy. A segmented method based on 1-bit compressed sensing was proposed for reducing reconstruction time. Simulation experiments verify the effectiveness of the proposed algorithm. This method can not only reduce time consumption, but also the required memory size. The stepped-frequency waveform is selected not only because it can make sampling easier than other waveforms, but also because it takes less time to recover the same scene when the stepped-frequency waveform is used instead of linear frequency modulation (LFM) waveform.

    Abstract

    In Synthetic Aperture Radar (SAR) systems, imaging algorithms based on 1-bit have a great advantage because the echo’s SNR is generally not high. However, those algorithms often require more time than the match filter strategy. A segmented method based on 1-bit compressed sensing was proposed for reducing reconstruction time. Simulation experiments verify the effectiveness of the proposed algorithm. This method can not only reduce time consumption, but also the required memory size. The stepped-frequency waveform is selected not only because it can make sampling easier than other waveforms, but also because it takes less time to recover the same scene when the stepped-frequency waveform is used instead of linear frequency modulation (LFM) waveform.
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    • References(9)
  • [1]
    BU H X, TAO R, BAI X, et al. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1003-1007.
    [2]
    ZHOU C B, ZHANG Z D, LIU F L. Robust 1-bit compressive sensing via variational Bayesian algorithm[J]. Digital Signal Processing, 2016, 50(C): 84-92.
    [3]
    周崇彬, 刘发林, 李博. 基于压缩感知的单比特合成孔径雷达成像算法[J]. 微波学报, 2015, 31(6): 71-77.
    ZHOU Chongbin, LIU Falin, LI Bo. A Compressive Sensing Method for One Bit Coded Synthetic Aperture Radar Imaging[J]. Journal of Microwaves, 2015, 31(6): 71-77.
    [4]
    FANG J, XU Z B, ZHANG B C, 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.
    [5]
    BARANIUK R, STEEGHS P. Compressive radar imaging[C]//2007 IEEE Radar Conference. Boston, USA: IEEE Press, 2007: 128-133.
    [6]
    GU F F, ZHANG Q, LOU H, et al. Two-dimensional sparse synthetic aperture radar imaging method with stepped-frequency waveform[J]. Journal of Applied Remote Sensing, 2015, 9(1): 096099(1-8).
    [7]
    YANG J P, THOMPSON J, HUANG X T, et al. Segmented reconstruction for compressed sensing SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7): 4214-4225.
    [8]
    BOUFOUNOS P T, BARANIUK R G. 1-bit compressive sensing[C]//Proceedings of the 42nd Annual Conference on Information Sciences and Systems. Princeton, USA: IEEE, 2008: 16-21.
    [9]
    JACQUES L, LASKA J N, BOUFOUNOS P T, et al. Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors[J]. IEEE Transactions on Information Theory, 2013, 59(4): 2082-2102.
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    [1]
    BU H X, TAO R, BAI X, et al. A novel SAR imaging algorithm based on compressed sensing[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(5): 1003-1007.
    [2]
    ZHOU C B, ZHANG Z D, LIU F L. Robust 1-bit compressive sensing via variational Bayesian algorithm[J]. Digital Signal Processing, 2016, 50(C): 84-92.
    [3]
    周崇彬, 刘发林, 李博. 基于压缩感知的单比特合成孔径雷达成像算法[J]. 微波学报, 2015, 31(6): 71-77.
    ZHOU Chongbin, LIU Falin, LI Bo. A Compressive Sensing Method for One Bit Coded Synthetic Aperture Radar Imaging[J]. Journal of Microwaves, 2015, 31(6): 71-77.
    [4]
    FANG J, XU Z B, ZHANG B C, 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.
    [5]
    BARANIUK R, STEEGHS P. Compressive radar imaging[C]//2007 IEEE Radar Conference. Boston, USA: IEEE Press, 2007: 128-133.
    [6]
    GU F F, ZHANG Q, LOU H, et al. Two-dimensional sparse synthetic aperture radar imaging method with stepped-frequency waveform[J]. Journal of Applied Remote Sensing, 2015, 9(1): 096099(1-8).
    [7]
    YANG J P, THOMPSON J, HUANG X T, et al. Segmented reconstruction for compressed sensing SAR imaging[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7): 4214-4225.
    [8]
    BOUFOUNOS P T, BARANIUK R G. 1-bit compressive sensing[C]//Proceedings of the 42nd Annual Conference on Information Sciences and Systems. Princeton, USA: IEEE, 2008: 16-21.
    [9]
    JACQUES L, LASKA J N, BOUFOUNOS P T, et al. Robust 1-bit compressive sensing via binary stable embeddings of sparse vectors[J]. IEEE Transactions on Information Theory, 2013, 59(4): 2082-2102.
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