Vol. 83

Latest Volume
All Volumes
All Issues

GPR Target Signal Enhancement Using Least Square Fitting Background and Multiple Clustering of Singular Values

By Budiman Putra Asmaur Rohman and Masahiko Nishimoto
Progress In Electromagnetics Research Letters, Vol. 83, 123-132, 2019


Ground penetrating radar is an effective nondestructive method for exploring subsurface object information by exploiting the differences in electromagnetic characteristics. However, this task is negatively affected by the existence of ground clutter and noise especially if the object is weak or/and shallowly buried. Therefore, this paper proposes a novel method for suppressing the clutter and background noise simultaneously in both flat and rough surfaces. First, the ground clutter is removed mainly by applying a simplified least square fitting background method, which remains the residual random noise signal. The remaining signal is then decomposed by singular value decomposition, which assumes that the decomposed signal contains four main components including strong target, weak target, very weak target, and accumulated noise signals. The powered singular values and their differences are clustered by K-means to extract the target signal components. The simulation results indicate that the proposed method is able to enhance the target signal with satisfactory results under both flat and rough surfaces as well as in a high-level background noise. Besides, this method also shows its superiority to the latest existing proposed methods.


Budiman Putra Asmaur Rohman and Masahiko Nishimoto, "GPR Target Signal Enhancement Using Least Square Fitting Background and Multiple Clustering of Singular Values," Progress In Electromagnetics Research Letters, Vol. 83, 123-132, 2019.


    1. Jol, H. M., Ground Penetrating Radar Theory and Applications, Elsevier, 2008.

    2. Mayordomo, A. M. and A. Yarovoy, "Optimal background subtraction in GPR for humanitarian demining," IEEE Radar Conference, 2008, EuRAD 2008, 48-51, European, 2008.

    3. Brooks, J. W., L. M. van Kempen, and H. Sahli, "Primary study in adaptive clutter reduction and buried minelike target enhancement from GPR data," Detection and Remediation Technologies for Mines and Minelike Targets V, Vol. 4038, 1183-1193, International Society for Optics and Photonics, 2000.

    4. Brunzell, H., "Detection of shallowly buried objects using impulse radar," IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 2, 875-886, 1999.

    5. Carevic, D., "Wavelet-based method for detection of shallowly buried objects from GPR data," Information, Decision and Control, 1999, IDC 99, Proceedings, 201-206, IEEE, 1999.

    6. Baili, J., S. Lahouar, M. Hergli, I. L. Al-Qadi, and K. Besbes, "GPR signal de-noising by discrete wavelet transform," Ndt & E International, Vol. 42, No. 8, 696-703, 2009.

    7. Abujarad, F., G. Nadim, and A. Omar, "Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (svd)," Proceedings of the 3rd International Workshop on Advanced Ground Penetrating Radar, 2005, IWAGPR 2005, 37-42, IEEE, 2005.

    8. Shen, J.-Q., H.-Z. Yan, and C.-Z. Hu, "Auto-selected rule on principal component analysis in ground penetrating radar signal denoising," Chinese Journal of Radio Science, Vol. 1, 17, 2010.

    9. Riaz, M. M. and A. Ghafoor, "Ground penetrating radar image enhancement using singular value decomposition," 2013 IEEE International Symposium on Circuits and Systems (ISCAS), 2388-2391, IEEE, 2013.

    10. Liu, C., C. Song, and Q. Lu, "Random noise de-noising and direct wave eliminating based on svd method for ground penetrating radar signals," Journal of Applied Geophysics, Vol. 144, 125-133, 2017.

    11. Zhu, J., W. Xue, X. Rong, and Y. Yu, "A clutter suppression method based on improved principal component selection rule for ground penetrating radar," Progress In Electromagnetics Research M, Vol. 53, 29-39, 2017.

    12. Soldovieri, F., A. F. Morabito, F. D’Agostino, S. I. Ivashov, V. V. Razevig, and I. A. Vasilyev, "A simple processing approach for holographic rascan data," Progress In Electromagnetics Research, Vol. 107, 315-330, 2010.

    13. Warren, C., A. Giannopoulos, and I. Giannakis, "gprmax: Open source software to simulate electromagnetic wave propagation for ground penetrating radar," Computer Physics Communications, Vol. 209, 163-170, 2016.

    14. Giannakis, I., A. Giannopoulos, and C. Warren, "A realistic fdtd numerical modeling framework of ground penetrating radar for landmine detection," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 9, No. 1, 37-51, 2016.