Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

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2.10

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Wei Chien1, Chien-Ching Chiu This email address is being protected from spambots. You need JavaScript enabled to view it.and Wei-Siang Gu2

1Department of College of Electric Information, Qinzhou University, Binhai Avenue, Qinzhou, Guangxi, P.R. China
2Department of Electrical Engineering, Tamkang University, Tamsui, Taiwan 251, R.O.C.


 

Received: March 29, 2017
Accepted: May 25, 2017
Publication Date: December 1, 2017

Download Citation: ||https://doi.org/10.6180/jase.2017.20.4.09  

ABSTRACT


A novel method for through-wall imaging (TWI) illuminated by the transverse electric (TE) waves is presented. Most microwave inverse scattering algorithms developed are for transverse magnetic (TM) wave illumination in which vector problem can be simplified to a scalar one, which less works have been reported on the more complicated TE case. In the TE case, the presence of polarization charges makes the inverse problem more nonlinear. This paper uses the self-adaptive dynamic differential evolution (SADDE) algorithm to recover the shapes of the two dimensional conducting cylinders by TE plane wave illumination. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equation is derived and the imaging problem is reformulated into optimization problem. The SADDE algorithm is employed to find out the global extreme solution of the object function. Numerical results show that the shapes of the conductor are well reconstructed. In addition, the effect of Gaussian noise on the reconstruction is investigated.


Keywords: Through-wall Imaging, Frequency-domain, Self-adaptive Dynamic Differential Evolution, Inverse Scattering


REFERENCES


  1. [1] Sabatier,P.C.,“TheoreticalConsiderations for Inverse Scattering,” Radio Science, Vol. 18, No. 1, pp. 629– 631 (1983). doi: 10.1029/RS018i001p00001
  2. [2] Storn, R. and Price, K., “Differential Evolution - a Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces,” Technical Report TR-95-012, International Computer Science Institute, Berkeley, Mar. (1995).
  3. [3] Kennedy,J.and Eberhart, R.C.,“Particle Swarm Optimization,”Proceedings of the IEEE International Conference on Neural Network, pp. 1942–1948 (1995). doi: 10.1109/ICNN.1995.488968
  4. [4] Rekanos, I. T., “Shape Reconstruction of a Perfectly Conducting Scatterer Using Differential Evolution and Particle Swarm Optimization,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 7, pp. 1967–1974 (2008). doi: 10.1109/TGRS.2008.916635
  5. [5] Michalski, K. A., “Electromagnetic Imaging of Circular-cylindrical Conductors and Tunnels Using a Differential Evolution Algorithm,” Microwave and Optical Technology Letters, Vol. 27, No. 5, pp. 330–334 (2000). doi: 10.1002/1098-2760(20001205)27:5<330 ::AID-MOP13>3.0.CO;2-H
  6. [6] Lee, Y. H., Cheng, Y. T., Chiu, C. C. and Chang, S. P., “Microwave Imaging for Half-space Imperfect Conductors,” Nondestructive Testing and Evaluation, Vol. 30, No. 1, pp. 4962 (2015). (EI) (SCI) doi: 10.1080/10589759.2014.992430
  7. [7] Chiang, J. S., Gu, W. S., Chiu, C. C. and Sun, C. H., “Estimation of the Two-dimensional Homogenous Dielectric Scatterer in a Slab Medium Using Particle Swarm Optimization and Asynchronous Particle Swarm Optimization,” Research in Nondestructive Evaluation, Vol. 26, No. 4, pp. 208–224 (2015). doi: 10.1080/ 09349847.2015.1024906
  8. [8] Yu, C. Y., Chiu, C. C., Chou, Y. K. and Shen, S. C., “Microwave Imaging in Frequency Domain for Through-wall Multiple Conductors,” Journal of Testing and Evaluation, Vol. 44, No. 4, pp. 16171623 (2016). (EI) (SCI) doi: 10.1520/JTE20140237
  9. [9] Ao, Y. Y. and Chi, H. Q., “Dynamic Differential Evolution for Constrained Real-parameter Optimization,” Journal of Advances in Information Technology, Vol. 1,No. 1,pp. 43–51(2010). doi:10.4304/jait.1.1.43-51
  10. [10] Cheng, Y. T., Chiu, C. C., Chang, S. P. and Hsu, J. C., “Comparison of Particle Swarm Optimization and Self-adaptive Dynamic Differential Evolution for the Imaging of a Periodic Conductor,” International Journal of Applied Electromagnetics and Mechanics, Vol. 46, No. 1, pp. 69–79 (2014).
  11. [11] Sun, C. H. and Chiu, C. C., “Inverse Scattering of Dielectric Cylindrical Target Using Dynamic Differential Evolution and Self-adaptive Dynamic Differential Evolution,” International Journal of RF and Microwave Computer-Aided Engineering,Vol.23, No. 5, pp. 579–585 (2013). doi: 10.1002/mmce.20692
  12. [12] Gennarelli, G., Vivone, G. and Braca, P., “Multiple Extended Target Tracking for Through-wall Radars,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 12, pp. 6482–6494 (2015). doi: 10. 1109/TGRS.2015.2441957
  13. [13] Tivive, F., Bouzerdoum, A. and Amin, M., “A Subspace Projection Approach for Wall Clutter Mitigation in Through-the-wall Radar Imaging,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 4, pp. 2108–2122 (2015). doi: 10.1109/TGRS.2014. 2355211
  14. [14] Dehmollaian, M. and Sarabandi, K., “Refocusing through Building Walls Using Synthetic Aperture Radar,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 46, No. 6, pp. 1589–1599 (2008).
  15. [15] Wang, Y. and Fathy, A. E., “Advanced System Level Simulation Platform for Three-dimensional UWB Through-wall Image,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 5, pp. 1986–2000 (2012). doi: 10.1109/TGRS.2011.2170694
  16. [16] Zhene, W., Zhao, Z. and Nie, Z., “Application of TRM in the UWB through wall radar,” Prog. Electromagn., Res., Vol. 87, pp. 279–296 (2008). doi: 10.2528/ PIER08101202
  17. [17] Gennarelli, G. and Soldovieri, F., “Radar Imaging through Cinderblock Walls: Achievable Performance by a Model-corrected Linear Inverse Scattering Approach,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 10, pp. 6738–6749 (2014). doi: 10.1109/TGRS.2014.2301851
  18. [18] Wan, Y., Yu, C. Y., Sun, C. H. and Chiu, C. C., “The Reconstruction of Time Domain Through-wall Imaging for a MetallicCylinder,” Imaging Science Journal, Vol. 63, No. 2, pp. 81–84 (2015). doi: 10.1179/ 1743131X14Y.0000000084
  19. [19] Lianlin, L., Zhang, W. and Li, F., “A Novel Autofocusing Approach for Real-time Through-wall Imaging under Unknown Wall Characteristics,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 48, No. 1, pp. 423–431 (2010). doi: 10.1109/TGRS.2009. 2024686
  20. [20] Kong, L. J., Cui, G. L., Yang, J. Y. and Yang, X. B., “Wall Parameters Estimation Method for Throughthe-wall Radar Imaging,” International Conference on Radar, pp. 297–301 (2008).
  21. [21] Li, C. L., Sun, C. H., Chiu, C. C. and Tuen, L. F., “Solving Inverse Scattering for a Partially Immersed Metallic Cylinder Using Steady-state Genetic Algorithm and Asynchronous Particle Swarm Optimization by TE Waves,” The Applied Computational ElectromagneticsSociety,Vol.28, No. 8,pp. 663–671 (2013).
  22. [22] Shen, J., Zhong, Y., Chen, X. and Ran, L., “Inverse Scattering Problems of Reconstructing Perfectly Electric Conductors with TE Illumination,” IEEE Transactions on Antennas and Propagation, Vol. 61, No. 9, pp. 4713–4721 (2013). doi: 10.1109/TAP.2013.2271891
  23. [23] Brest, J., Greiner, S., Boskovic, B., Mernik, M. and Zumer, V., “Self-adapting Control Parameters in Differential Evolution: Comparative Study on Numerical Benchmark Problems,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 6, pp. 646–657 (2006). doi: 10.1109/TEVC.2006.872133
  24. [24] Goudos, S. K., Siakavara, K., Samaras, T., Vafiadis, E. E. and Sahalos, J. N., “Self-adaptive Differential Evolution Applied to Real-valued Antenna and Microwave Design Problems,”IEEETransactions on Antennas and Propagation, Vol. 59, No. 4, pp. 1286–1298 (2011). doi: 10.1109/TAP.2011.2109678