volume | PIER Journals

Abstract

In this paper, a novel approach based on the support vector machine (SVM) for dielectric target detection in through-wall scenario is proposed. Through-wall detection is converted to the establishment and use of a mapping between backscattered data and the dielectric parameter of the target. Then the propagation effects caused by walls, such as refraction and speed change, are included in the mapping that can be regressed after SVM training process. The training and testing data for the SVM is obtained by finite-difference time-domain (FDTD) simulation. Numerical experiments show that once the training phase is completed, this technique only needs computational time in an order of seconds to predict the parameters. Besides, experimental results show that good consistency between the actual parameters and estimated ones is achieved. Through-wall target tracking is also discussed and the results are acceptable.

1. Ferris, D. D. and N. C. Currie, "Survey of current technologies for through-the-wall surveillance (TWS)," Proceedings of SPIE, 1999. Google Scholar

2. Baranoski, E. J., "Through-wall imaging: Historical perspective and future directions," Journal of the Franklin Institute-Engineering and Applied Mathematics, Vol. 345, 556-569, 2008.
doi:10.1016/j.jfranklin.2008.01.005 Google Scholar

3. Ahmad, F., G. J. Frazer, S. A. Kassam, and M. G. Amin, "Design and implementation of near-field, wideband synthetic aperture beamformers," IEEE Transactions on Aerospace and Electronic Systems, Vol. 40, 206-220, 2004.
doi:10.1109/TAES.2004.1292154 Google Scholar

4. Ahmad, F., M. G. Amin, and S. A. Kassam, "Synthetic aperture beamformer for imaging through a dielectric wall," IEEE Transactions on Aerospace and Electronic Systems, Vol. 41, 271-283, 2005.
doi:10.1109/TAES.2005.1413761 Google Scholar

5. Wang, G. Y. and M. G. Amin, "Imaging through unknown walls using different standoff distances," IEEE Transactions on Signal Processing, Vol. 54, 4015-4025, 2006.
doi:10.1109/TSP.2006.879325 Google Scholar

6. Ahmad, F., M. G. Amin, and G. Mandapati, "Autofocusing of through-the-wall radar imagery under unknown wall characteristics," IEEE Transactions on Image Processing, Vol. 16, 1785-1795, 2007.
doi:10.1109/TIP.2007.899030 Google Scholar

7. Soldovieri, F. and R. Solimene, "Through-wall imaging via a linear inverse scattering algorithm," IEEE Geoscience and Remote Sensing Letters, Vol. 4, 513-517, 2007.
doi:10.1109/LGRS.2007.900735 Google Scholar

8. Song, L. P., C. Yu, and Q. H. Liu, "Through-wall imaging (TWI) by radar: 2-D tomographic results and analyses," IEEETransactions on Geoscience and Remote Sensing, Vol. 43, 2793-2798, 2005.
doi:10.1109/TGRS.2005.857914 Google Scholar

9. Dehmollaian, M. and K. Sarabandi, "Refocusing through building walls using synthetic aperture radar," IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, 1589-1599, 2008.
doi:10.1109/TGRS.2008.916212 Google Scholar

10. Li, L. L., W. J. Zhang, and F. Li, "A novel autofocusing approach for real-time through-wall imaging under unknown wall characteristics," IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, 423-431, 2010.
doi:10.1109/TGRS.2009.2024686 Google Scholar

11. Zhang, W., A. Hoorfar, and L. Li, "Through-the-wall target localization with time reversal music method," Progress In Electromagnetics Research, Vol. 106, 75-89, 2010.
doi:10.2528/PIER10052408 Google Scholar

12. Bermani, E., A. Boni, S. Caorsi, and A. Massa, "An innovative real-time technique for buried object detection," IEEE Transactions on Geoscience and Remote Sensing, Vol. 41, 927-931, 2003.
doi:10.1109/TGRS.2003.810928 Google Scholar

13. Bermani, E., A. Boni, S. Caorsi, M. Donelli, and A. Massa, "A multi-source strategy based on a learning-by-examples technique for buried object detection," Progress In Electromagnetics Research, Vol. 48, 185-200, 2004.
doi:10.2528/PIER03110701 Google Scholar

14. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of SVM-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.
doi:10.2528/PIER04090801 Google Scholar

15. Zhang, Q. H., B. X. Xiao, and G. Q. Zhu, "Inverse scattering by dielectric circular cylinder using support vector machine approach," Microwave and Optical Technology Letters, Vol. 49, 372-375, 2007.
doi:10.1002/mop.22131 Google Scholar

16. Vapnik, V. N., The Nature of Statistical Learning Theory, Springer Verlag, 2000.

17. Smola, A. J. and B. Scholkopf, "A tutorial on support vector regression," Statistics and Computing, Vol. 14, 199-222, 2004.
doi:10.1023/B:STCO.0000035301.49549.88 Google Scholar

18. Kim, Y. and H. Ling, "Through-wall human tracking with multiple Doppler sensors using an artificial neural network," IEEE Transactions on Antennas and Propagation, Vol. 57, 2116-2122, 2009.
doi:10.1109/TAP.2009.2021871 Google Scholar

19. Du, K. L., M. N. S. Swamy, and I. Ebrary, Neural Networks in a Softcomputing Framework, Springer, 2006.

20. Massa, A., A. Boni, and M. Donelli, "A classification approach based on SVM for electromagnetic subsurface sensing," IEEE Transactions on Geoscience and Remote Sensing, Vol. 43, 2084-2093, 2005.
doi:10.1109/TGRS.2005.853186 Google Scholar

Dr. Fang-Fang Wang

Dr. Ye-Rong Zhang