A new method of high resolution ISAR imaging under low SNR based on improved compressive sensing

被引：2
|
作者：
Zhang L. [1 ,2 ]
Zhang L. [1 ,2 ]
Xing M.-D. [1 ]
机构：
[1] Key Laboratory of Radar Signal Processing, Xidian University
[2] College of Electronics and Information, Xi'an Polytechnic University
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2010年 / 32卷 / 09期
关键词：
Compressive sensing (CS); ISAR; Limited pulses; Radar imaging; Super resolution;
D O I：
10.3724/SP.J.1146.2009.01256
中图分类号：
学科分类号：
摘要：
In consideration of the the issue of the weakness performance of Compressive Sensing (CS) ISAR imaging under low SNR condition, an improved CS method is proposed in this paper. Energy based threshold is proposed to identify range cells containing only noise, a coherent projection in cross-range to improve the SNR of measurements then a iterative re-weighting l1 norm optimization is applied to enhance the signal recovery while reject noise. Moreover, an advantage of this robustness is the ability to produce a high quality image and is suitable for ISAR imaging by using very limited echoes under strong noise and clutter. Experimental result of real data processing verifies the proposed method's capability of depressing noise or clutter and extracting strong scatterers to construct high resolution image. In the experiment, high quality image can be generated by using only 16 pulses and its robustness is confirmed too.
引用
收藏
页码：2263 / 2267
页数：4
相关论文
共 9 条
  • [1] Liu Y.-B., Li Y.-C., Xing M.-D., Detection of weak ship target based on taper scale transform, Journal of Electronics & Information Technology, 31, 11, pp. 2575-2580, (2009)
  • [2] Lazarov A.D., Iterative MMSE method and recurrent kalman procedure for ISAR image reconstruction, IEEE Transactions on Aerospace and Electronic System, 37, 4, pp. 1432-1440, (2001)
  • [3] Wang Y., Jiang Y.-C., The ISAR imaging of ship based on adaptive chirplet decomposition, Journal of Electronics & Information Technology, 28, 6, pp. 982-984, (2006)
  • [4] Donoho D., Compressed sensing, IEEE Transactions on Information Theory, 52, 4, pp. 5406-5425, (2006)
  • [5] Zhang L., Xing M.D., Qui C.W., Et al., Achieving higher resolution ISAR imaging with limited pulses via compressed sampling, IEEE Transactions on Geoscience and Remote Sensing Letter, 6, 3, pp. 567-571, (2009)
  • [6] Li Y.-C., Xing M.-D., Zhang L., Et al., Detection, parameter estimation and imaging of maneuvering target in wide-band signal, Science in Chian Series F: Information Sciences, 52, 6, pp. 1015-1026, (2009)
  • [7] Blumensath T., Davies D.M.E., Stagewise weak gradient pursuits. Part II: Theoretical properties, (2008)
  • [8] Grant M., Boyd S., Ye Y., cvx: Matlab software for disciplined convex programming, (2009)
  • [9] Candes E., Romberg J., Tao T., Near-optimal signal recovery from random projections: Universal encoding strategies, IEEE Transactions on Information Theory, 52, 2, pp. 489-509, (2006)
1