Sparse-aperture ISAR imaging algorithm

被引：0
|
作者：
Zeng C. [1 ]
Zhu W. [2 ]
Jia X. [2 ]
机构：
[1] Graduate School, Space Engineering Univ., Beijing
[2] Dept. of Electronic and Optical Engineering, Space Engineering Univ., Beijing
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2019年 / 46卷 / 03期
关键词：
Adaptive filtering; Compressed sensing; Inverse synthetic aperture radar; Multiple measurement vectors; Smoothed zero norm; Sparse aperture;
D O I：
10.19665/j.issn1001-2400.2019.03.019
中图分类号：
学科分类号：
摘要：
Under sparse aperture conditions, some problems arise with inverse synthetic aperture radar imaging such as low azimuth resolution and susceptibility to noise. To solve them, the two-dimensional sparseness of a target is used to transform the imaging problem into the sparse signal reconstruction problem under the multiple measurement vectors model. The zero norm-least mean square algorithm is processed in parallel to improve the efficiency. The optimal step-size formula is used instead of the fixed step-size to avoid the influence of the improper step-size on the convergence speed and accuracy. And the smoothed zero norm is introduced to approximate the zero norm to improve the reconstruction accuracy and noise immunity ability. In comparison with existing methods, the proposed algorithm can obtain a clearer target image, which is robust to noise and requires less computation. The effectiveness of the proposed method is verified by simulation and the real data processing result. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：123 / 129
页数：6
相关论文
共 12 条
  • [1] Chen Q., Xing M., Li H., Et al., Cross-range Scaling for ISAR Imaging within Short CPI and Low SNR, Journal of Xidian University, 41, 6, pp. 12-17, (2014)
  • [2] Giusti E., Cataldo D., Bacci A., Et al., ISAR Image Resolution Enhancement: Compressive Sensing versus State-of-the-art Super-resolution Techniques, IEEE Transactions on Aerospace and Electronic Systems, 54, 4, pp. 1983-1997, (2018)
  • [3] Candes E.J., Romberg J., Tao T., Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2006)
  • [4] Wang L., Zhu D., Ma K., Et al., Sparse Imaging of Space Targets Using Kalman Filter, Journal of Electronics and Information Technology, 40, 4, pp. 846-852, (2018)
  • [5] Xu G., Yang L., Bi G.A., Et al., Enhanced ISAR Imaging and Motion Estimation with Parametric and Dynamic Sparse Bayesian Learning, IEEE Transactions on Computational Imaging, 3, 4, pp. 940-952, (2017)
  • [6] Gu Y.T., Jin J., Mei S.L., l<sub>0</sub> Norm Constraint LMS Algorithm for Sparse System Identification, IEEE Signal Processing Letters, 16, 9, pp. 774-777, (2009)
  • [7] Jin J., Gu Y.T., Mei S.L., A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework, IEEE Journal of Selected Topics in Signal Processing, 4, 2, pp. 409-420, (2010)
  • [8] Li S., Chen W., Yang J., Et al., Study on the Fast Sparse Recovery Algorithm via Multiple Measurement Vectors of Arbitrary Sparse Structure, Acta Electronica Sinica, 43, 4, pp. 708-715, (2015)
  • [9] Peng J., Han Z., You X., Et al., A Signal Recovery Algorithm on Multiple Measurement Vectors of Arbitrary Sparse Structure with Impulsive Noise, Journal of Harbin Engineering University, 38, 11, pp. 1806-1811, (2017)
  • [10] Chen W., Li S., Yang J., Fast Recovery Algorithm for Complex Sparse Signal with Arbitrary Sparse Structure and Its Inverse Synthetic Aperture Radar Imaging, Journal of Optoelectronics • Laser, 26, 4, pp. 797-804, (2015)
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