Multiple measurement vectors block sparse signal recovery ISAR imaging algorithm

被引：0
|
作者：
Feng J. [1 ,2 ]
Zhang G. [1 ,2 ]
机构：
[1] College of Electronics and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing
来源：
| 1959年 / Chinese Institute of Electronics卷 / 39期
关键词：
Block sparse signal; Inverse synthetic aperture radar (ISAR); Multiple measurement vectors (MMV); Smoothed function;
D O I：
10.3969/j.issn.1001-506X.2017.09.07
中图分类号：
学科分类号：
摘要：
In order to obtain fast inverse synthetic aperture radar (ISAR) sparse images with finite pulse, a multiple measurement vectors (MMV) model block sparse signal recovery ISAR imaging algorithm is proposed by utilizing the block structure of targets. Firstly, the MMV sparse imaging model is established, ISAR imaging is converted into the MMV block sparse signal recovery problem. Then, one negative exponential function sequence is used as the smoothed function to approach the block L0 norm, the optimization solution of the smoothed function is obtained by constructing a decreasing sequence, the solution is projected into the feasible set by the gradient projection algorithm. Finally, the revised step is added to ensure the searching direction of the optimization value of the block sparse signal is the steepest descent gradient direction. Simulation results verify the proposed algorithm has advantages in imaging time and imaging quality. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：1959 / 1964
页数：5
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共 20 条
  • [1] Chen W.F., Li S.D., Yang J., 2D complex sparse reconstruction algorithm with LBI and its application, Acta Automatica Sinica, 42, 4, pp. 556-565, (2016)
  • [2] Wang Y.H., Zhang J.Q., A greedy refinement Bayesian approach to joint sparse signal recovery, Acta Electronic Sinica, 44, 4, pp. 780-787, (2016)
  • [3] Thakshila W., Hao C., Pramod K.V., Performance limits of compressive sensing-based signal classification, IEEE Trans. on Signal Processing, 60, 6, pp. 2758-2770, (2012)
  • [4] Needell D., Vershynin R., Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit, IEEE Journal of Selected Topics in Signal Processing, 4, 2, pp. 310-316, (2010)
  • [5] Duan H.J., Zhu D.Y., Li Y., Et al., Recovery and imaging for missing data of the strip-map SAR based on compressive sensing, Systems Engineering and Electronics, 38, 5, pp. 1025-1031, (2016)
  • [6] Cetin M., Stojanovic I., Onhon N., Et al., Sparsity-driven synthetic aperture radar imaging, IEEE Signal Processing Magazine, 31, 4, pp. 27-40, (2014)
  • [7] Pan X.Y., Wang W., Yang G.Y., Sub-Nyquist sampling jamming against ISAR with CS-based HRRP reconstruction, IEEE Sensors Journal, 16, 6, pp. 1597-1602, (2016)
  • [8] Zhang Y.H., Xing M.D., Xu G., Joint sparsity constraint interferometric ISAR imaging for 3-D geometry of maneuvering targets with sparse apertures, Journal of Electronics & Information Technology, 37, 9, pp. 2151-2157, (2015)
  • [9] Tan X., Roberts W., Li J., Et al., Sparse learning via iterative minimization with application to mimo radar imaging, IEEE Trans. on Signal Processing, 59, 3, pp. 1088-1101, (2011)
  • [10] Hux W., Tong N.N., Zhang Y.S., Et al., Multiple-input-multiple-output radar super resolution three-dimensional imaging based on a dimension-reduction compressive sensing, IET Radar, Sonar & Navigation, 10, 4, pp. 757-764, (2015)
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