Dielectric Objects Reconstruction by Combining Subspace-based Algorithm with Randomly Global Optimization Algorithm
碩士 === 淡江大學 === 電機工程學系碩士班 === 104 === This thesis presents the two-dimensional electromagnetic imaging problem by Subspace-based algorithm. Subspace-based algorithm is different with methods of processing inverse scattering problem by contrast source inversion (CSI). The essence of the subspace-base...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/06070010340065824096 |
| Summary: | 碩士 === 淡江大學 === 電機工程學系碩士班 === 104 === This thesis presents the two-dimensional electromagnetic imaging problem by Subspace-based algorithm. Subspace-based algorithm is different with methods of processing inverse scattering problem by contrast source inversion (CSI). The essence of the subspace-based optimization method is that part of the contrast source is determined from the spectrum analysis without using any optimization when the rest is determined by optimization method. By applying the singular value decomposition (SVD) to the field equation, the induced current is divided into the signal space and the noise space. This feature can reduce the number of unknowns and computing costs to speed up the convergence of the algorithm. We also transform the inverse scattering problem into optimization problem and solved by Self-Adaptive Dynamic Differential Evolution (SADDE). SADDE can process numerous unknowns of electromagnetic imaging problems. Different scatterers and environment will be used to investigate whether Subspace-based algorithm can keep stability of reconstruction or not. We will also compare Genetic Algorithm (GA) to show the robustness and the searching speed of SADDE.
|
|---|