Time Domain Image Reconstruction Of 2-D Inhomogenous Dielectric Cylinders Buried in a Slab Medium

碩士 === 淡江大學 === 電機工程學系碩士班 === 100 === This paper presents the studies of microwave image reconstructions that are approached based on the time-domain technique (finite difference time domain, FDTD) and optimization method for 2-D inhomogeneous dielectric cylinders. The dielectric cylinder is buried...

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Bibliographic Details
Main Authors: Jyun-Fu Li, 李俊甫
Other Authors: Chien-Ching Chiu丘建青
Format: Others
Language:zh-TW
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/91734386414215234498
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Summary:碩士 === 淡江大學 === 電機工程學系碩士班 === 100 === This paper presents the studies of microwave image reconstructions that are approached based on the time-domain technique (finite difference time domain, FDTD) and optimization method for 2-D inhomogeneous dielectric cylinders. The dielectric cylinder is buried in a slab media. For the forward scattering the FDTD method is employed to calculate the scattered E fields, while for the inverse scattering asynchronous particle swarm optimization (APSO) is utilized to determine the permittivity of the cylindrical scatterer with arbitrary cross section. In order to explore the unknown dielectric cylinder in a three-layered slab medium, an electromagnetic pulse can be conducted to illuminate the cylinder, for which the scattered E fields can then be measured. The inverse problem is then resolved by an optimization approach. The idea is to perform the image reconstruction by utilization of Asynchronous Particle Swarm Optimization to minimize the discrepancy between the measured and calculated scattered field data. Three times,five times and ten times of the unknows population size are also investigated. The suitability and efficiency of applying APSO for microwave imaging of 2D dielectric cylinders are examined in this dissertation. Numerical results show that even when the initial guesses are far away from the exact one, good reconstruction can be obtained by Asynchronous Particle Swarm Optimization. However, the APSO can reduce the convergent speed in terms of the number of the objective function calls.