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1887

access icon free Fast 2D super resolution ISAR imaging method under low signal-to-noise ratio

© The Institution of Engineering and Technology
Received 20/01/2017, Accepted 15/06/2017, Revised 30/05/2017, Published 19/06/2017

Sparse representation (SR)-based inverse synthetic aperture radar (ISAR) imaging method can achieve super-resolution image of a target. However, it is computationally expensive and sensitive to noise. To overcome these two drawbacks, the authors propose the coupled Nesterov linearised Bregman iteration algorithm based on two–dimensional (2D) real-valued dictionaries (CNLBI-TRD) for ISAR imaging. First, the ISAR echoes are taken as a 2D joint SR model in the range frequency-azimuth Doppler domain. Then the 2D complex-valued dictionaries are converted into real-valued ones through unitary transformations. The computational complexity is thus decreased by a factor of at least four. Finally, the CNLBI algorithm is proposed to reconstruct the 2D SR model directly. It combines the Nesterov's accelerated gradient method with the condition number optimisation of sensing matrices. An adaptive-adjustment strategy of the soft threshold parameter is presented. Thus the total iteration numbers can be greatly reduced. The simulation results and real data experiments verify the effectiveness of the proposed imaging algorithm.

Inspec keywords: image representation; communication complexity; image resolution; Doppler radar; image reconstruction; optimisation; gradient methods; radar resolution; radar imaging; synthetic aperture radar; matrix algebra

Other keywords: soft threshold parameter; coupled Nesterov linearised Bregman iteration algorithm; signal-to-noise ratio; 2D joint SR model; 2D complex-valued dictionary; two-dimensional real-valued dictionary; CNLBI-TRD algorithm; computational complexity; adaptive-adjustment strategy; 2D real-valued dictionary; inverse synthetic aperture radar; range frequency-azimuth Doppler domain; image resolution; Nesterov accelerated gradient method; fast 2D super resolution ISAR imaging method; sparse representation model; sensing matrix optimization

Subjects: Optimisation techniques; Interpolation and function approximation (numerical analysis); Linear algebra (numerical analysis); Optical, image and video signal processing; Radar equipment, systems and applications

References

    1. 1)
      • 22. Ender, J.: ‘On compressive sensing applied to radar’, Signal Process., 2010, 90, (5), pp. 1402–1414.
    2. 2)
      • 21. Keh, C.H., Chien, C.Y.: ‘A unitary transformation method for angle-of-arrival estimation’, IEEE Trans. Signal Process., 1991, 39, (4), pp. 975–977.
    3. 3)
      • 10. Hongchao, L., Bo, J., Hongwei, L., et al: ‘Superresolution ISAR imaging based on sparse Bayesian learning’, IEEE Trans. Geosci. Remote Sens., 2014, 52, (8), pp. 5005–5013.
    4. 4)
      • 28. Yin, Wotao.: ‘Analysis and generalizations of the linearized Bregman method’, SIAM J. Imaging Sci., 2010, 3, (4), pp. 856–877.
    5. 5)
      • 29. Arian, M.: ‘Approximate message passing algorithms for compressed sensing’. PhD thesis, Stanford University, 2010.
    6. 6)
      • 20. Sharif-Nassab, A., Kharratzadeh, M., Babaie-Zadeh, M., et al: ‘How to use real-valued sparse recovery algorithms for complex-valued sparse recovery’. 20th European Signal Processing Conf., August 2012, pp. 849–853.
    7. 7)
      • 27. Bo, H., Shiqian, M., Donald, G.: ‘Accelerated linearized Bregman method’, J. Sci. Comput., 2013, 54, pp. 428–453.
    8. 8)
      • 11. Hongchao, L., Bo, J., Hongwei, L., et al: ‘A novel ISAR imaging algorithm for micromotion targets based on multiple sparse Bayesian learning’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (10), pp. 1772–1776.
    9. 9)
      • 2. Ling, H., Fengzhou, D., Hongwei, L.: ‘A fused-lasso-based Doppler imaging algorithm for spinning targets with occlusion effect’, IEEE Signal Process. Mag., 2014, 6, (5), pp. 125–138.
    10. 10)
      • 5. Chao, S., Baoping, W., Yang, F., et al: ‘High-resolution ISAR imaging of maneuvering targets based on sparse reconstruction’, Signal Process., 2015, 108, pp. 535–548.
    11. 11)
      • 24. Wotao, Y., Stanley, O., Donald, G., et al: ‘Bregman iterative algorithms for l1-minimization with applications to compressed sensing’, SIAM J. Imaging Sci., 2008, 1, (1), pp. 143–168.
    12. 12)
      • 15. Wang, H.X., Quan, Y.H., Xing, M.D., et al: ‘ISAR imaging via sparse probing frequencies’, IEEE Geosci. Remote Sens. Lett., 2014, 8, (3), pp. 451–455.
    13. 13)
      • 26. Cai, J.F., Osher, S., Shen, Z. W.: ‘Linearized Bregman iterations for frame-based image deblurring’, SIAM J. Imaging Sci., 2009, 2, (1), pp. 226–252.
    14. 14)
      • 14. Zhen, L., Xizhang, W., Xiang, L.: ‘Decoupled ISAR imaging using RSFW based on twice compressed sensing’, IEEE Trans. Aerosp. Electron. Syst., 2014, 50, (4), pp. 3195–3211.
    15. 15)
      • 7. Hongtao, L., Chaoyu, W., Ke, W., et al: ‘High resolution range profile of compressive sensing radar with low computational complexity’, IET Radar Sonar Navig., 2015, 9, (8), pp. 984–990.
    16. 16)
      • 6. Krichene, H.A., Pekala, M.J., Sharp, M.D., et al: ‘Compressive sensing and stretch processing’. 2011 IEEE Radar Conf., May 2011, pp. 362–367.
    17. 17)
      • 12. Huiping, D., Lizao, Z., Jun, F., et al: ‘Pattern-coupled sparse Bayesian learning for inverse synthetic aperture radar imaging’, IEEE Geosci. Remote Sens. Lett., 2014, 11, (10), pp. 1772–1776.
    18. 18)
      • 17. Shunsheng, Z., Wei, Z., Zhulin, Z., et al: ‘High-resolution bistatic ISAR imaging based on two-dimensional compressed sensing’, IEEE Trans. Antennas Propag., 2015, 63, (5), pp. 2098–2111.
    19. 19)
      • 1. Herman, M.A., Strohmer, T.: ‘High-resolution radar via compressed sensing’, IEEE Trans. Signal Process., 2009, 57, (6), pp. 2275–2284.
    20. 20)
      • 23. Ghaffari, A., Babaie-Zadeh, M., Jutten, C.: ‘Sparse decomposition of two dimensional signals’. 20th European Signal Processing Conf., April 2009, pp. 3157–3160.
    21. 21)
      • 30. Lei, Z., Zhijun, Q., Mengdao, X., et al: ‘High-resolution ISAR imaging by exploiting sparse apertures’, IEEE Trans. Antennas Propag., 2012, 60, (2), pp. 997–1008.
    22. 22)
      • 13. Gang, X., Mengdao, X., Lei, Z.: ‘Sparse apertures ISAR imaging and scaling for maneuvering targets’, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 2014, 7, (7), pp. 2942–2956.
    23. 23)
      • 19. Daiyin, Z., Ling, W., Yusheng, Y., et al: ‘Robust ISAR range alignment via minimizing the entropy of the average range profile’, IEEE Geosci. Remote Sens. Lett., 2009, 6, (2), pp. 204–208.
    24. 24)
      • 8. Min, L., Gongjian, Z., Bin, Z., et al: ‘Sparse representation denoising for radar high resolution range profiling’, Int. J. Antennas Propag., 2014, 2, pp. 1–8.
    25. 25)
      • 9. Lei, Z., Mengdao, X., ChengWei, Q., et al: ‘Resolution enhancement for inversed synthetic aperture radar imaging under low SNR via improved compressive sensing’, IEEE Trans. Geosci. Remote Sens., 2010, 48, (10), pp. 3824–3838.
    26. 26)
      • 4. Müjdat, Ç., Ivana, S., Özben, Ö., et al: ‘Sparsity-driven synthetic aperture radar imaging’, IEEE Signal Process. Mag., 2014, 31, (4), pp. 27–40.
    27. 27)
      • 16. Gang, G.L., Hao, Z., Xiqin, W., et al: ‘ISAR 2-D imaging of uniformly rotating targets via matching pursuit’, IEEE Trans. Aerosp. Electron. Syst., 2012, 48, (2), pp. 1838–1846.
    28. 28)
      • 18. Shiyong, L., Guoqiang, Z., Houmin, L., et al: ‘Near-field radar imaging via compressive sensing’, IEEE Trans. Antennas Propag., 2015, 63, (2), pp. 828–833.
    29. 29)
      • 3. Sonia, T., Alessio, B., Elisa, G., et al: ‘Compressive sensing-based inverse synthetic radar imaging from incomplete data’, IET Radar Sonar Navig., 2016, 10, (2), pp. 386–397.
    30. 30)
      • 25. Osher, S., Yu, M., Dong, B., et al: ‘Fast linearized Bregman iteration for compressive sensing and sparse denoising’ (University of California Los Angeles, Department of Mathematics, Los Angeles, 2008), pp. 1–18.
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