GPR imaging algorithm based on compressive sensing

被引：1
|
作者：
Zhou L. [1 ]
Wang H.-J. [1 ]
Su Y. [1 ]
机构：
[1] School of Electronic Science and Engineering, National University of Defense Technology
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2011年 / 33卷 / 09期
关键词：
Back projection algorithm; Compressive sensing; Ground penetrating radar imaging; Nyquist sampling theorem;
D O I：
10.3969/j.issn.1001-506X.2011.09.15
中图分类号：
学科分类号：
摘要：
The Nyquist sampling theorem must be satisfied in traditional data acquisition of the ground penetrating radar (GPR), which degrades the imaging efficiency dramatically. However, the theory of compressive sensing (CS) shows that sparse signals can be precisely reconstructed by solving a convex l1 minimization problem at a rate significantly below the Nyquist rate, and it can overcome the shortcomings of traditional data acquisition. The CS theory is applied into the GPR imaging, and the effects on imaging results caused by the dimension of measurement matrix, signal to noise ratio (SNR), incomplete data and compactness of targets are analyzed systematically through the simulated data. Experimental results show that compared with the traditional GPR imaging algorithm, the proposed algorithm has higher precision and fewer false alarms. This algorithm is also robust to noise and incomplete data, and saves the resources of data storage and acquisition.
引用
收藏
页码：1995 / 2001
页数：6
相关论文
共 18 条
  • [1] Daniels D., Applications of impulse radar technology, Proc. of the International Conference on Radar, pp. 667-672, (1997)
  • [2] Grasmueck M., Weger R., Horstmeyer H., Full-resolution 3D GPR imaging for geoscience and archeology, Proc. of the 10th International Conference on Ground Penetrating Radar, pp. 329-332, (2004)
  • [3] Halman J.I., Shubert K.A., Ruck G.T., SAR processing of ground-penetrating radar data for buried UXO detection: results from a surface-based system, IEEE Trans. on Antennas and Propagation, 46, 7, pp. 1023-1027, (1998)
  • [4] Zetic R., Sachs J., Thoma R., Modified cross-correlation back projection for uwb imaging: numerical examples, Proc. of the IEEE International Conference on Ultra-Wideband, pp. 650-654, (2005)
  • [5] Meincke P., Linear GPR imaging based on electromagnetic plane-wave spectra and diffraction tomography, Proc. of the 10th International Conference on Ground Penetrating Radar, pp. 55-58, (2004)
  • [6] Hansen T.B., Johansen P.M., Inversion scheme for ground penetrating radar that takes into account the planar air-soil interface, IEEE Trans. on Geoscience and Remote Sensing, 38, 1, pp. 496-506, (2000)
  • [7] Gu K., Wang G., Li J., Migration based SAR imaging for ground penetrating radar systems, IEE Radar, Sonar, and Navigation, 151, 5, pp. 317-325, (2004)
  • [8] Xu X., Miller E.L., Optimization of migration method to locate buried object in lossy medium, Proc. of IEEE International Geoscience and Remote Sensing Symposium, pp. 337-339, (2002)
  • [9] Fisher E., McMechan G.A., Annan A.P., Examples of reverse-time migration of single-channel, ground-penetrating radar profiles, Geophysics, 57, 4, pp. 577-586, (1992)
  • [10] Leuschen C.J., Plumb R.G., A matched-filter-based reverse-time migration algorithm for ground-penetrating radar data, IEEE Trans. on Geoscience and Remote Sensing, 39, 5, pp. 929-936, (2001)
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