Vol. 23

Front:[PDF file] Back:[PDF file]
Latest Volume
All Volumes
All Issues
2012-02-08

3D Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing

By Ji-Liang Cai, Chuang-Ming Tong, Wei-Jun Zhong, and Wei-Jie Ji
Progress In Electromagnetics Research M, Vol. 23, 153-165, 2012
doi:10.2528/PIERM11121206

Abstract

Long data collecting time is one of the bottlenecks of the stepped-frequency continuous-wave ground penetrating radar (SFCW-GPR). We discuss the applicability of the Compressive Sensing (CS) method to three dimensional buried point-like targets imaging for SFCW-GPR. It is shown that the image of the sparse targets can be reconstructed by solving a constrained convex optimization problem based on l1norm} minimization with only a small number of data from randomly selected frequencies and antenna scan positions, which will reduce the data collecting time. Target localization ability, performance in noise, the effect of frequency bandwidth, and the effect of the wave travel velocity in the soil are demonstrated by simulated data. Numerical results show that the presented CS method can reconstruct the point-like targets in the right position even with 10% additive Gaussian white noise and some wave travel velocity estimation error. p

Citation


Ji-Liang Cai, Chuang-Ming Tong, Wei-Jun Zhong, and Wei-Jie Ji, "3D Imaging Method for Stepped Frequency Ground Penetrating Radar Based on Compressive Sensing," Progress In Electromagnetics Research M, Vol. 23, 153-165, 2012.
doi:10.2528/PIERM11121206
http://www.jpier.org/PIERM/pier.php?paper=11121206

References


    1. Grandjean, G., J. Gourry, and A. Bitri, "Evaluation of GPR techniques for civil-engineering applications: Study on a test site," J. Appl. Geophys., Vol. 45, No. 3, 141-156, 2000.
    doi:10.1016/S0926-9851(00)00021-5

    2. Feng, X. and M. Sato, "Pre-stack migration applied to GPR for landmine detection," Inverse Prob., Vol. 20, 99-115, 2004.
    doi:10.1088/0266-5611/20/6/S07

    3. Groenenboom, J. and A. Yarovoy, "Data processing and imaging in GPR system dedicated for landmine detection," Subsurf. Sens. Technol. Appl., Vol. 3, No. 4, 387-402, 2002.
    doi:10.1023/A:1020321632316

    4. Hubbard, S., C. Jinsong, K. Williams, Y. Rubin, and J. Peterson, "Environmental and agricultural applications of GPR," Proc. 3rd Int. Workshop on Adv. Ground Penetrating Radar, 45-49, 2005.
    doi:10.1109/AGPR.2005.1487843

    5. Daniels, D., Ground Penetrating Radar, 2nd edition, London, UK, 2004.
    doi:10.1049/PBRA015E

    6. Counts, T., A. C. Gurbuz, W. R. Scott, Jr., J. H. McClellan, and K. Kangwook, "Multistatic ground-penetrating radar experiments," IEEE Trans. Geosci. Remote Sens., Vol. 45, No. 8, 2544-2553, Aug. 2007.
    doi:10.1109/TGRS.2007.900677

    7. Lopera, O., E. C. Slob, N. Milisavljevic, and S. Lambot, "Filtering soil surface and antenna effects from GPR data to enhance landmine detection," IEEE Trans. Geosci. Remote Sens., Vol. 45, No. 3, 707-717, 2007.
    doi:10.1109/TGRS.2006.888136

    8. Fang, G.-Y. and M. Sato, "Stepped frequency ground penetrating radar and its application for landmine detection," Acta Electronica Sinica, Vol. 33, No. 3, 436-439, 2005.

    9. Gurbuz, A. C., J. H. McClellan, and W. R. Scott, "A compressive sensing data acquisition and imaging method for stepped frequency GPRs," IEEE Transactions on Signal Processing, Vol. 57, No. 7, 2640-2650, 2009.
    doi:10.1109/TSP.2009.2016270

    10. Donoho, D. L., "Compressive sensing," IEEE Trans. on Information. Theory, Vol. 52, No. 4, 1289-1306, 2006.
    doi:10.1109/TIT.2006.871582

    11. Baraniuk, R. and P. Steeghs, "Compressive radar imaging," Proc. IEEE Radar Conf., 128-133, 2007.

    12. Yu., H.-M. and Y. Fang, "Research on compressive sensing based 3D imaging method applied to ground penetrating radar," Journal of Electronics & Information Technology, Vol. 32, No. 1, 12-16, 2010.
    doi:10.3724/SP.J.1146.2009.00040

    13. Huang, Q., L. Qu, B. Wu, and G. Fang, "UWB through-wall imaging based on compressive sensing," IEEE Trans. Geosci. Remote Sens., Vol. 48, No. 3, 1408-1415, 2010.
    doi:10.1109/TGRS.2009.2030321

    14. Wei, S.-J., X.-L. Zhang, J. Shi, and G. Xiang, "Sparse reconstruction for SAR imaging based on compressed sensing," Progress In Electromagnetics Research, Vol. 109, 63-81, 2010.
    doi:10.2528/PIER10080805

    15. Zhang, L., et al., "Achieving higher resolution ISAR imaging with limited pulses via compressed sampling," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 3, 567-571, 2009.
    doi:10.1109/LGRS.2009.2021584

    16. Candes, E. J. and M. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, 2130, Mar. 2008.

    17. Johansson, E. M. and J. E. Mast, "Three dimensional ground penetrating radar imaging using a synthetic aperture time-domain focusing," Proc. SPIE Conf. Adv. Microw. Millimeter Wave Detectors, Vol. 2275, 205-214, 1994.

    18. Candes, E. and T. Tao, "The Dantzig selector: Statistical estimation when p is much larger than n," Ann. Statist., Vol. 35, No. 6, 2313-2351, 2007.
    doi:10.1214/009053606000001523

    19. Tuncer, M. A. C. and A. C. Gurbuz, "Ground reflection removal in compressive sensing ground penetrating radars," IEEE Geoscience and Remote Sensing Letters, 2011.

    20. Picardi, M., "Background subtraction techniques - A review," Proc. IEEE Int. Conf. Syst. Man. Cybern., 3099-3104, Oct. 10-13, 2004.

    21. Mayordomo, A. M. and A. Yarovoy, "Optimal background subtraction in GPR for humanitarian demining," Proc. 5th Eur. Radar Conf., 48-51, Oct. 2008.

    22. Grant, M. and S. Boyd, , CVX: Matlab Software for Disciplined Convex Programming (Web Page and Software), 2011, Available: http://stanford.edu/ boyd/cvx..