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All issues Volume 42 / No 5 (October 2024) JNWPU, 42 5 (2024) 875-881 References
Open Access
Issue
JNWPU
Volume 42, Number 5, October 2024
Page(s) 875 - 881
DOI https://doi.org/10.1051/jnwpu/20244250875
Published online 06 December 2024
  1. BAO Zheng, XING Mengdao, WANG Tong. Radar imaging technology[J]. Beijing: Publishing House of Electronic Industry, 2005 (in Chinese) [Google Scholar]
  2. LU Baoguo, LIANG Bo, MA Huanfang. Method for aircraft target recognition and classification in optical remote sensing image[J]. Command Information System and Technology, 2020, 11(5): 78–82 (in Chinese) [Google Scholar]
  3. XU Ying, GU Yu, PENG Dongliang. SAR image recognition based on sparse representation and multi-feature fusion[J]. Command Information System and Technology, 2020, 11(3): 29–35 (in Chinese) [Google Scholar]
  4. GUILLAUME H, RENE G. High resolution snapshot SAR/ISAR imaging of ship targets at sea[J]. SPIE Remote Sensing, 2003, 488339–47 [Google Scholar]
  5. LAZAROV A, MINCHEV C N. ISAR image reconstruction and autofocusing procedure over phase modulated signals[J]. Radar, 2002, 490: 536–541 [Google Scholar]
  6. DONOHO D L. Compressed sensing[J]. IEEE Trans on Information Theory, 2006, 52(4): 1289–1306 [Article] [CrossRef] [Google Scholar]
  7. CANDES E J, ROMBERG J, TAO T. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information[J]. IEEE Trans on Information Theory, 2006, 52(2): 489–509 [Article] [CrossRef] [Google Scholar]
  8. CANDES E J, ROMBERG J K, TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications on Pure and Applied Mathematics, 2006, 59(8): 1207–1223 [Article] [CrossRef] [Google Scholar]
  9. WEHNER D R. High-resolution radar[M]. 2nd ed. Boston, MA: Artech House, Inc, 1994 [Google Scholar]
  10. BARANIUK R, STEEGHS P. Compressive radar imaging[C]//The 2007 IEEE Radar Conference, 2007: 128-133 [Google Scholar]
  11. POTTER L C, PARKER J T. Sparsity and compressed sensing in radar imaging[J]. Proceedings of the IEEE, 2010, 98(6): 1006–1020 [Article] [CrossRef] [Google Scholar]
  12. PATEL V M, EASLEY G R, HEALY D M. Compressed synthetic aperture radar[J]. IEEE Journal of Selected Topics in Signal Processing, 2010, 4(2): 244–254 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  13. TROPP J A. Greed is good: algorithmic results for sparse approximation[J]. IEEE Trans on Information Theory, 2004, 50(10): 2231–2242 [Article] [CrossRef] [Google Scholar]
  14. DAUBECHIES I, TESCHKE G, VESE L A. Iteratively solving linear inverse problems under general convex constraints[J]. Inverse Problems and Imaging, 2007, 1(1): 29–46 [Article] [CrossRef] [Google Scholar]
  15. DONOHO D L. For most large underdetermined systems of linear equations the minimal l1-norm solution is also the sparsest solution[J]. Communications on Pure and Applied Mathematics, 2006, 59(6): 797–829 [Article] [CrossRef] [MathSciNet] [Google Scholar]
  16. BOYD S, VANDENBERGHE L. Convex optimization[J]. Cambridge: Cambridge University Press, 2004 [CrossRef] [Google Scholar]
  17. ZHANG L, QIAO Z J, XING M D. High-resolution isar imaging by exploiting sparse apertures[J]. IEEE Trans on Antennas and Propagation, 2012, 60(2): 997–1008 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  18. ZHANG L, QIAO Z J, XING M D. High-resolution ISAR imaging with sparse stepped-frequency waveforms[J]. IEEE Trans on Geoscience and Remote Sensing, 2011, 49(11): 4630–4651 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  19. ZHANG L, XING MD, QIU C W. Achieving higher resolution ISAR imaging with limited pulses via compressed sampling[J]. IEEE Geoscience and Remote Sensing Letters, 2009, 6(3): 567–571 [Article] [CrossRef] [Google Scholar]
  20. BOYD S, PARIKH N, CHU Eet al. Distributed optimization and statistical learning via the alternating direction method of multipliers[J]. Foundations and Trends in Machine Learning, 2011, 3(1): 1–122 [Google Scholar]
  21. CHAMBOLLE A, VORE D E, NAM-YONG R Aet al. Nonlinear wavelet image processing: variational problems, compression, and noise removal through wavelet shrinkage[J]. IEEE Trans on Image Processing, 1998, 7(3): 319–335 [Article] [CrossRef] [Google Scholar]
  22. DAUBECHIES Ingridj, DEFRISE Michel, DEMOL Christine. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint[J]. Communications on Pure and Applied Mathematics, 2004, 57(11): 1413–1457 [Article] [CrossRef] [Google Scholar]
  23. CHAMBOLLE A, POCK T. A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal of Mathematical Imaging and Vision, 2011, 40(1): 120–145 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  24. ESSER E, ZHANG X, CHAN T F. A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science[J]. SIAM Journal on Imaging Sciences, 2010, 3(4): 1015–1046 [Article] [CrossRef] [Google Scholar]
  25. CANDES E J, WAKIN M B, BOYD S P. Enhancing sparsity by reweighted l1 minimization[J]. Journal of Fourier Analysis and Applications, 2008, 14(5): 877–905 [CrossRef] [Google Scholar]
  26. CHARTRAND R, STANEVA V. Restricted isometry properties and nonconvex compressive sensing[J]. Inverse Problems, 2008, 24(3): 035020 [Article] [CrossRef] [Google Scholar]
  27. FOUCART S, LAI M J. Sparsest solutions of underdetermined linear systems via lq-minimization for 0 < q < 1[J]. Applied and Computational Harmonic Analysis, 2009, 26(3): 395–407 [CrossRef] [Google Scholar]
  28. SUN Q. Recovery of sparsest signals via lq-minimization[J]. Applied and Computational Harmonic Analysis, 2012, 32(3): 329–341 [Article] [CrossRef] [Google Scholar]
  29. FIGUEIREDO M A T, BIOUCAS-DIAS J M, NOWAK R D. Majorization-minimization algorithms for wavelet-based image restoration[J]. IEEE Trans on Image Processing, 2007, 16(12): 2980–2991 [CrossRef] [Google Scholar]

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