Hostname: page-component-76fb5796d-45l2p Total loading time: 0 Render date: 2024-04-29T19:29:38.568Z Has data issue: false hasContentIssue false

Microwave imaging based on two hybrid particle swarm optimization approaches

Published online by Cambridge University Press:  21 November 2018

Bouzid Mhamdi*
Affiliation:
Syscom Laboratory, Engineer School of Tunis, BP 37 Belvedere, Tunis - 1002, Tunisia
*
Author for correspondence: Bouzid Mhamdi, E-mail: bmhamdi@cte.edu.sa

Abstract

In this paper, the solution of the inverse scattering problem for determining the shape and location of perfectly conducting scatterers by making use of electromagnetic scattered fields is presented. Based on the boundary condition and the measured scattered field, a set of nonlinear integral equations is derived and the imaging problem is reformulated into an optimization one. Then, two evolutionary algorithms are used to solve the inverse scattering problem. To further clarify, our contribution is to test two well-known algorithms in the literature to the problem of microwave imaging. The hybrid approaches combine the standard particle swarm optimization (PSO) with the ideas of the simulated annealing and extremal optimization algorithms, respectively. Both of them are shown to be more efficient than original PSO technique. Reconstruction results by using the two presented schemes are compared with exact shapes of some conducting cylinders; and good agreements with the original shapes are observed.

Type
Research Papers
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2018 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.HoLmann, B and Scherzer, O (1994) Factors influencing the illposedness of nonlinear problems. Inverse Problems 10, 12771297.Google Scholar
2.Qing, A and Jen, L (1997) A novel method for microwave imaging of dielectric cylinder in layered media. Journal of Electromagnetic Waves and Applications 11, 13371348.Google Scholar
3.Van den berg, PM and Van der Horst, M (1995) Nonlinear inversion in induction logging using the modified gradient method. Radio Science 30, 13551369.Google Scholar
4.Garnero, L, Franchois, A, Hugonin, JP, Pichot, C and Joachimowicz, N (1991) Microwave imaging-complex permittivity reconstruction-by simulated annealing. IEEE Trans. Microwave Theory and Techniques 39, 18011807.Google Scholar
5.Brovko Alexander, V, Murphy Ethan, K and Yakovlev Vadim, V (2009) Waveguide microwave imaging: neural network reconstruction of functional 2-D permittivity profiles. IEEE Transactions on Microwave Theory and Techniques 57, 406414.Google Scholar
6.Chien, W (2008) Inverse scattering of an un-uniform conductivity scatterer buried in a three-layer structure. Progress in Electromagnetics Research, PIER 82, 118.Google Scholar
7.Krraoui, H, Mejri, T and Aguili, T (2016) Dielectric constant measurement of materials by a microwave technique: application to the characterization of vegetation leaves. Journal of Electromagnetic Waves and Applications 30, 16431660.Google Scholar
8.Chiu, C-C and Liu, P (1996) Image reconstruction of a perfectly conducting cylinder by the genetic algorithm. IET Microwaves, Antennas & Propagation, 143, 249253.Google Scholar
9.Qing, A (2006) Dynamic differential evolution strategy and applications in electromagnetic inverse scattering problems. IEEE Transactions on Geoscience and Remote Sensing 44, 116125.Google Scholar
10.Rekanos, I (2008) Shape reconstruction of a perfectly conducting scatterer using differential evolution and particle swarm optimization. IEEE Transactions on Geoscience and Remote Sensing 46, 19671974.Google Scholar
11.Semnani, A, Rekanos, IT, Kamyab, M and Papadopoulos, TG (2010) Two-dimensional microwave imaging based on hybrid scatterer representation and differential evolution. IEEE Transactions on Antennas and Propagation 58, 32893298.Google Scholar
12.Rocca, P, Benedetti, M, Donelli, M, Franceschini, D and Massa, A (2009) Evolutionary optimization as applied to inverse scattering problems. Inverse Problems in Science and Engineering 25, 123003–1123003–44.Google Scholar
13.Rocca, P, Oliveri, G and Massa, A (2011) Differential evolution as applied to electromagnetics. IEEE Transactions on Antennas and Propagation 53, 3849.Google Scholar
14.Kennedy, J and Eberhart, RC (1995) Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks 4, 19421948.Google Scholar
15.Huang, C-H, Chiu, C-C, Li, C-L and Chen, K-C (2008) Time domain inverse scattering of a two-dimensional homogenous dielectric object with arbitrary shape by particle swarm optimization. Progress in Electromagnetics Research, PIER 82, 381400.Google Scholar
16.Donelli, M and Massa, A (2005) Computational approach based on a particle swarm optimizer for microwave imaging of two-dimensional dielectric scatterers. IEEE Transactions on Microwave Theory and Techniques 53, 17611776.Google Scholar
17.Semnani, A and Kamyab, M (2008) Truncated cosine Fourier series expansion method for solving 2-D inverse scattering problems. Progress in Electromagnetics Research, PIER 81, 7397.Google Scholar
18.Randazzo, A (2012) Swarm optimization methods in microwave imaging. International Journal of Microwave Science and Technology 2012, Article ID 491713, 12.Google Scholar
19.Kumar, A, Bhattacharya, A and Singh, DK (2013) Microwave image reconstruction of two dimension dielectric scatterers using swarm particle optimization. IJECET 4, 5761.Google Scholar
20.Chiang, J-S, Gu, W-S, Chiu, C-C and Sun, C-H (2015) 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 26, 208224.Google Scholar
21.Bindu, G and Mathew, KT (2004) Characterization of behind and malignant breast tissues using 2-D microwave tomographic imaging. Microwave and Optical Technology Letters 49, 23412345.Google Scholar
22.Suganthan, PN, Hansen, N, Liang, JJ, Deb, K, Chen, Y-P, Auger, A and Tiwari, S (2005) Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization, Technical Report, Nanyang Technological University, Singapore and IIT Kanpur, India.Google Scholar
23.Chen, M-R, Li, X, Zhang, X and Lu, Y-Z (2010) A novel particle swarm optimizer hybridized with extremal optimization. Applied Soft Computing 10, 367373.Google Scholar
24.Harrington, RF (1993) Field Computation by Moment Method. New York, USA: Wiley IEEE Press.Google Scholar
25.Tezel, NS and Şimşek, S (2007) Neural network approach to shape reconstruction of a conducting cylinder. Journal of Electrical & Electronics Engineering 7, 299304.Google Scholar
26.Ülker, ED and Ülker, S (2014) Application of particle swarm optimization to microwave tapered microstrip lines. CSEIJ 4, 5964.Google Scholar
27.Kirkpatrick, S, Gelatt, CD Jr and Vecchi, MP (1983) Optimization by simulated annealing. Journal of Science 220, 671680.Google Scholar
28.Taheri, J and Zomaya, AY (2005) A simulated annealing approach for mobile location management. Proceedings of the 19th IEEE International Parellel and Distributed Processing Symposium (IPDPS).Google Scholar
29.Suman, B and Kumar, P (2006) A survey of simulated annealing as a tool for single and multi-objective optimization. Journal of the Operational Research Society 57, 11431160.Google Scholar
30.Boettcher, S and Percus, AG (2000) Nature's way of optimizing. Artificial Intelligence 119, 275286.Google Scholar
31.Mhamdi, B, Grayaa, K and Aguili, T (2011) Microwave imaging of dielectric cylinders from experimental scattering data based on the genetic algorithms, neural networks and a hybrid micro genetic algorithm with conjugate gradient. International Journal of Electronics and Communications 65, 140147.Google Scholar