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Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques

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Abstract

The sparse recovery algorithms formulate synthetic aperture radar (SAR) imaging problem in terms of sparse representation (SR) of a small number of strong scatters’ positions among a much large number of potential scatters’ positions, and provide an effective approach to improve the SAR image resolution. Based on the attributed scatter center model, several experiments were performed with different practical considerations to evaluate the performance of five representative SR techniques, namely, sparse Bayesian learning (SBL), fast Bayesian matching pursuit (FBMP), smoothed l0 norm method (SL0), sparse reconstruction by separable approximation (SpaRSA), fast iterative shrinkage-thresholding algorithm (FISTA), and the parameter settings in five SR algorithms were discussed. In different situations, the performances of these algorithms were also discussed. Through the comparison of MSE and failure rate in each algorithm simulation, FBMP and SpaRSA are found suitable for dealing with problems in the SAR imaging based on attributed scattering center model. Although the SBL is time-consuming, it always get better performance when related to failure rate and high SNR.

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References

  1. GERRY M J, POTTER L C, INDER J G, ANDRIA V M. A parametric model for synthetic aperture radar measurements [J]. IEEE Transactions on Antennas and Propagation, 1999, 47(7): 1179–1188.

    Article  Google Scholar 

  2. MALIOUTOV D M, CETIN M, WILLSKY A S. Sparse signal reconstruction perspective for source localization with sensor arrays [J]. IEEE Trans Signal Process, 2005, 53(8): 3010–3022.

    Article  MathSciNet  Google Scholar 

  3. COTTER S F, RAO B D, ENGAN K, KREUTZ-DELGADO K. Sparse solutions to linear inverse problems with multiple measurement vectors [J]. IEEE Trans Signal Process, 2005, 53(7): 2477–2488.

    Article  MathSciNet  Google Scholar 

  4. IVANA S, WILLIAM C K. Imaging of moving targets with multi-static SAR using an overcomplete dictionary [J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(1): 164–176.

    Article  Google Scholar 

  5. ZOU Fei, LI Xiang, ROBERTO T. Inverse synthetic aperture radar imaging based on sparse signal processing [J]. Journal of Central South University of Technology, 2011, 18(5): 1609–1613.

    Article  Google Scholar 

  6. CETIN M, KARL W C. Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization [J]. IEEE Trans Image Process, 2001, 10(4): 623–631.

    Article  MATH  Google Scholar 

  7. WEI Shun-jun, ZHANG Xiao-ling. Sparse reconstruction for SAR imaging based on compressed sensing [J]. Progress in Electromagnetics Research, 2010, 109: 63–81.

    Article  Google Scholar 

  8. SAMADI S, CETIN M, MASNADI-SHIRAZI M A. Sparse representation-based synthetic aperture radar imaging [J]. IET Radar, Sonar and Navigation, 2011, 5(2): 182–193.

    Article  Google Scholar 

  9. XU Jiang-pin, PI Yi-ning, CAO Zong-jie. Bayesian compressive sensing in synthetic aperture radar imaging [J]. IET Radar Sonar Navig, 2012, 6(1): 2–8.

    Article  Google Scholar 

  10. CHEN Zhao-fu, TAN Xing, XUE Ming, LI Jian. Bayesian SAR imaging [C]// Frederick D. Garber, Proceeding of the SPIE, Algorithms for Synthetic Aperture Radar Imagery XVII. Orlando, Florida: SPIE, 2010: 1–12.

    Google Scholar 

  11. POTTER L C, ERTIN E, PARKER J, CETIN M. Sparsity and compressed sensing in radar imaging [J]. Proc IEEE, 2010, 98(6): 1006–1020.

    Article  Google Scholar 

  12. VISHAL M P, GLENN R E, DENNIS M H, RAMA C. Compressed synthetic aperture radar [J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 244–254.

    Article  Google Scholar 

  13. XUE Ming, SANTIAGO E, SEDEHI M, TAN Xing, LI Jian. SAR imaging via iterative adaptive approach and sparse Bayesian learning [C]// Proceeding of the SPIE, Algorithms for Synthetic Aperture Radar Imagery XVI. Orlando, Florida: SPIE, 2009: 1–12.

    Google Scholar 

  14. VARSHNEY K R, CETIN M, FISHER J W, WILLSKY A S. Sparse representation in structured dictionaries with application to synthetic aperture radar [J]. IEEE Trans on Signal Processing, 2008, 56(8): 3548–3561.

    Article  MathSciNet  Google Scholar 

  15. LIU Zhi-xue, LI Gang, ZHANG Hao, WANG Xi-qin. Sparse-driven sar imaging using MMV-StOMP [C]// 1st International Workshop on Compressed Sensing applied to Radar. Bonn, Germany: IEEE Press, 2012: 14–16.

    Google Scholar 

  16. YANG Zhao-cheng, LIU Zheng, LI Xiang, NIE Lei. Performance analysis of stap algorithms based on fast sparse recovery techniques [J]. Progress in Electromagnetics Research B, 2012, 41: 251–268.

    Article  Google Scholar 

  17. TIPPING M E. Sparse Bayesian learning and the relevance vector machine [J]. Journal of Machine Learning Research, 2001, 2(1): 211–244.

    MathSciNet  Google Scholar 

  18. WIPF D P, RAO B D. Sparse Bayesian learning for basis selection [J]. IEEE Trans on Signal Processing, 2004, 52(8): 2153–2164.

    Article  MathSciNet  Google Scholar 

  19. SCHNITER P, POTTER L C, ZINIEL J. Fast Bayesian matching pursuit [C]// Proc Workshop on Information Theory and Application (ITA). La Jolla, CA: IEEE Press, 2008: 326–333.

    Google Scholar 

  20. HOSEIN M G, BABAIE-ZADEH M, JUTTEN C. A fast approach for overcomplete sparse decomposition based on smoothed l0 norm [J]. IEEE Trans Signal Process, 2009, 57(1): 289–301.

    Article  MathSciNet  Google Scholar 

  21. WRIGHT S J, NOWAK R D, FIGUEIREDO M A T. Sparse reconstruction by separable approximation [J]. IEEE Trans Signal Process, 2009, 57(7): 2479–2493.

    Article  MathSciNet  Google Scholar 

  22. BECK A, TEBOULLE M. A fast iterative shrinkage-thresholding algorithm for linear inverse problems [J]. SIAM J Imag Sci, 2009, 2(1): 183–202.

    Article  MATH  MathSciNet  Google Scholar 

  23. TROPP J A, WRIGHT J. Computational methods for sparse solution of linear inverse problems [J]. Proc of IEEE, 2010, 98(6): 948–958.

    Article  Google Scholar 

  24. TROPP J A, GILBERT A C. Signal recovery from random measurements via orthogonal matching pursuit [J]. IEEE Trans Inform Theory, 2007, 53(12): 4655–4666.

    Article  MathSciNet  Google Scholar 

  25. NEEDELL D, TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples [J]. Appl and Comp Harmonic Anal, 2009, 26(3): 301–321.

    Article  MATH  MathSciNet  Google Scholar 

  26. FIGUEIREDO M A T, NOWAK R D, WRIGHT S J. Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems [J]. IEEE Journal of Selected Topics in Signal Processing, 2007, 1(4): 586–597.

    Article  Google Scholar 

  27. QIU Kun, DOGANDZIC A. Variance-component based sparse signal reconstruction and model selection [J]. IEEE Trans Signal Process, 2010, 58(6): 2935–2952.

    Article  MathSciNet  Google Scholar 

  28. ZHANG Zhi-ling, RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning [J]. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5): 912–926.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electronic Science and Engineering, National University of Defense Technology, Changsha, 410073, China

    Wu-ge Su  (苏伍各), Hong-qiang Wang  (王宏强) & Zhao-cheng Yang  (阳召成)

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  1. Wu-ge Su  (苏伍各)
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  2. Hong-qiang Wang  (王宏强)
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  3. Zhao-cheng Yang  (阳召成)
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Corresponding author

Correspondence to Wu-ge Su  (苏伍各).

Additional information

Foundation item: Project(61171133) supported by the National Natural Science Foundation of China; Project(11JJ1010) supported by the Natural Science Fund for Distinguished Young Scholars of Hunan Province, China; Project(61101182) supported by National Natural Science Foundation for Young Scientists of China

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Su, Wg., Wang, Hq. & Yang, Zc. Synthetic aperture radar imaging based on attributed scatter model using sparse recovery techniques. J. Cent. South Univ. 21, 223–231 (2014). https://doi.org/10.1007/s11771-014-1933-4

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  • DOI: https://doi.org/10.1007/s11771-014-1933-4

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