Microwave imaging by mixed-order discontinuous Galerkin contrast source inversion

2014 ◽  
Author(s):  
Ian Jeffrey ◽  
Amer Zakaria ◽  
Joe LoVetri
Keyword(s):  
Discontinuous Galerkin ◽  
Microwave Imaging ◽  
Source Inversion ◽  
Contrast Source Inversion ◽  
Mixed Order

Finite-element contrast source inversion method for microwave imaging

2010 ◽  
Vol 26 (11) ◽  
pp. 115010 ◽  
Author(s):  
Amer Zakaria ◽  
Colin Gilmore ◽  
Joe LoVetri
Keyword(s):  
Finite Element ◽  
Microwave Imaging ◽  
Inversion Method ◽  
Source Inversion ◽  
Contrast Source Inversion

Contrast Source Inversion Algorithm Applied in 3-D Microwave Imaging

2012 ◽  
Vol 468-471 ◽  
pp. 314-317
Author(s):  
Yu Xin Xie ◽  
Xue Jing Wang ◽  
Jing Hong Miao ◽  
Cui Juan Guo ◽  
Jiang Xiong Li ◽  
...  
Keyword(s):  
Integral Equation ◽  
Three Dimensional ◽  
Iterative Algorithms ◽  
Microwave Imaging ◽  
Controlled Environment ◽  
Source Inversion ◽  
Inversion Algorithm ◽  
3D Inversion ◽  
Contrast Source Inversion ◽  
Dielectric Target

This paper describes an nonlinear three-dimensional (3D) inversion algorithm for reconstructing a homogeneous dielectric target in an anechoic chamber in a controlled environment. The applied algorithm is a nonlinear iterative algorithms—two domain integral equation based method, namely the contrast source inversion (CSI) algorithm. The inversion results, which are presented and compared for the co-polorized data using the multi-frequency multi-bistatic measurements, verify the capability, the accuracy of the CSI algorithm for 3-D microwave imaging.

scholarly journals On the Numerical Implementation of the Multiplicative Regularization in Microwave Imaging

2020 ◽  
Author(s):  
Puyan Mojabi
Keyword(s):  
High Spatial Frequency ◽  
Positive Definite ◽  
Microwave Imaging ◽  
Numerical Implementation ◽  
Discrete Operator ◽  
Source Inversion ◽  
Contrast Source Inversion ◽  
L2 Norm ◽  
Frequency Components ◽  
Multiplicative Regularization

<p> We consider the widely-used weighted L2 norm total variation multiplicative regularizer (MR) for both the Gauss-Newton inversion (GNI) and contrast source inversion (CSI) algorithms in microwave imaging (MWI). It is shown that the proper numerical implementation of the discretized MR operator is important for the GNI algorithm whereas the CSI algorithm is more robust with respect to different implementations of this MR. For the GNI algorithm, the MR operator should be discretized such that high spatial frequency components are not present in its nullspace, and also the resulting discrete operator is positive definite.</p>

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