volume | PIER Journals

Abstract

Principal component analysis is usually used for clutter suppression of ground penetrating radar, but its performance is influenced by the selection of main components of target signal. In the paper, an improved principal component selection rule is proposed for selecting the main components of target signal. In the method, firstly difference spectrum of singular value is used to extract direct wave and strong target signal, and then, Fuzzy-C means clustering algorithm is used to determine the weights of principal component of weak target signal. Finally, the principal components of strong target signal and weak target signal are reconstructed to obtain target signal. Experimental results show that the proposed method can effectively remove the clutter signals and reserve more target information.

1. Daniels, D. J., Surface-Penetrating Radar, 2nd Ed., IEEE Press, 2004.
doi:10.1049/PBRA015E

2. Jol, H. M., Ground Penetrating Radar: Theory and Applications, Elsevier Science, 2009.

3. Chen, C. S. and Y. Jeng, "Nonlinear data processing method for the signal enhancement of GPR data," Journal of Applied Geophysics, Vol. 75, No. 1, 113-123, 2011.
doi:10.1016/j.jappgeo.2011.06.017 Google Scholar

4. Soldovieri, F., I. Catapano, P. M. Barone, S. E. Lauro, E. Mattei, E. Pettinelli, G. Valerio, D. Comite, and A. Galli, "GPR estimation of the geometrical features of buried metallic targets in testing conditions," Progress In Electromagnetics Research B, Vol. 49, 339-362, 2013.
doi:10.2528/PIERB12120508 Google Scholar

5. Yavuz, M. E., A. E. Fouda, and F. L. Teixeira, "GPR signal enhancement using sliding-window space-frequency matrices," Progress In Electromagnetics Research, Vol. 145, No. 2, 1-10, 2014.
doi:10.2528/PIER14010105 Google Scholar

6. Brunzell, H., "Detection of shallowly buried objects using impulse radar," IEEE Trans. Geosci. Remote Sens., Vol. 37, No. 2, 875-886, March 1999.
doi:10.1109/36.752207 Google Scholar

7. Brooks, J. W., L. M. V. Kempen, and H. Sahli, "Primary study in adaptive clutter reduction and buried minelike target enhancement from GPR data," Proc. SPIE, Vol. 4038, 1183-1192, 2000.
doi:10.1117/12.396226 Google Scholar

8. Luo, Y. and G. Y. Fang, "GPR clutter reduction and buried target detection by improved Kalman filter technique," Proc. of 2005 IEEE Int. Conf. Machine Learning and Cybernetics., Vol. 9, 5432-5436, 2005. Google Scholar

9. Carevic, D., "Wavelet-based method for detection of shallowly buried objects from GPR data," Proceedings on Information, Decision and Control, 201-206, 1999.
doi:10.1109/IDC.1999.754154 Google Scholar

10. Baili, J., S. Lahouar, M. Hergli, I. L. Al-Qadi, and K. Besbes, "GPR signal de-noising by discrete wavelet transform," NDT and E International, Vol. 42, No. 8, 696-703, December 2009.
doi:10.1016/j.ndteint.2009.06.003 Google Scholar

11. Bao, Q. Z., Q. C. Li, and W. C. Chen, "GPR data noise attenuation on the curvelet transform," Applied Geophysics, Vol. 11, No. 3, 301-310, September 2014.
doi:10.1007/s11770-014-0444-2 Google Scholar

12. Osjooi, B., M. Julayusefi, and A. Goudarzi, "GPR noise reduction based on wavelet thresholdings," Arabian Journal of Geosciences, Vol. 8, No. 5, 2937-2951, May 2015.
doi:10.1007/s12517-014-1339-5 Google Scholar

13. Gunatilaka, A. H. and B. A. Baertlein, "Subspace decomposition technique to improve gpr imaging of antipersonnel mines," Proc. SPIE 4038, Detection and Remediation Technologies for Mines and Minelike Targets, Vol. V, 1008-1018, August 2000. Google Scholar

14. Abujarad, F., A. Jostingmeier, and A. S. Omar, "Clutter removal for landmine using different signal processing techniques," Proc. of the Tenth IEEE Int. Conf. Ground Penetrating Radar, 697-700, June 2004. Google Scholar

15. Lee, K. C., J. S. Qu, and M. C. Fang, "Application of SVD noise-reduction technique to PCA based radar target recognition," Progress In Electromagnetics Research, Vol. 81, 447-459, 2008.
doi:10.2528/PIER08032101 Google Scholar

16. Nan, F. Y., S. Y. Zhou, Y. N. Wang, F. H. Li, and W. F. Yang, "Reconstruction of GPR signals by spectral analysis of the svd components of the data matrix," IEEE Geosci. Remote Sens. Lett., Vol. 7, No. 1, 200-204, January 2010.
doi:10.1109/LGRS.2009.2031657 Google Scholar

17. Liu, H. B., X. Wang, and M. Zheng, "A clutter suppression method of ground penetrating radar for detecting shallow surface target," IET International Radar Conference 2015, 1-4, October 2015. Google Scholar

18. Karlsen, B., J. Larsen, H. B. D. Sorensen, and K. B. Jakobsen, "Comparison of PCA and ICA based clutter reduction in GPR systems for anti-personal landmine detection," Proc. 11th IEEE Signal Processing Workshop on Statistical Signal Processing, 146-149, 2001.
doi:10.1109/SSP.2001.955243 Google Scholar

19. Abujarad, F., G. Nadim, and A. Omar, "Clutter reduction and detection of landmine objects in ground penetrating radar data using singular value decomposition (SVD)," Proc. of the 3rd Int. Workshop on Advanced Ground Penetrating Radar, 37-42, May 2005. Google Scholar

20. 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. 25, No. 1, 83-87, February 2010. Google Scholar

21. Grzegorczyk, T. M., B. Zhang, and M. T. Cornick, "Optimized SVD approach for the detection of weak subsurface targets from ground-penetrating radar data," IEEE Trans. Geosci. Remote Sens., Vol. 51, No. 3, 1635-1642, 2013.
doi:10.1109/TGRS.2012.2207906 Google Scholar

22. Riaz, M. M. and A. Ghafoor, "Ground penetrating radar image enhancement using singular value decomposition," IEEE Int. Symp. Circuits & Systems, 2388-2391, 2013. Google Scholar

23. Bezdek, J. C., R. Ehrlich, and W. Full, "FCM: The fuzzy c-means clustering algorithm," Computers & Geosciences, Vol. 10, No. 2-3, 191-203, 1984.
doi:10.1016/0098-3004(84)90020-7 Google Scholar

24. Pal, N. R. and J. C. Bezdek, "On cluster validity for the fuzzy c-means model," IEEE Trans. Fuzzy Syst., Vol. 3, No. 3, 370-379, 1995.
doi:10.1109/91.413225 Google Scholar

25. Miyamoto, S., H. Ichihashi, and K. Honda, Algorithms for Fuzzy Clustering --- Methods in c-Means Clustering with Applications, Springer, 2008.

Dr. Jichao Zhu

Dr. Wei Xue

Dr. Xia Rong

Dr. Yunyun Yu