A Multiplicative Regularized Gauss-Newton Inversion for Shape and Location Reconstruction

被引：36
|
作者：
Mojabi, Puyan [1 ]
LoVetri, Joe [1 ]
Shafai, Lotfollah [1 ]
机构：
[1] Univ Manitoba, Dept Elect & Comp Engn, Winnipeg, MB R3T 5V6, Canada
来源：
IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION | 2011年 / 59卷 / 12期
基金：
加拿大自然科学与工程研究理事会;
关键词：
Gauss-Newton inversion; microwave tomography; regularization; CONTRAST SOURCE INVERSION; ELECTROMAGNETIC MEASUREMENTS; SCATTERING; FRAMEWORK; TE;
D O I：
10.1109/TAP.2011.2165487
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
A multiplicative regularized Gauss-Newton inversion algorithm is proposed for shape and location reconstruction of homogeneous targets with known permittivities. The data misfit cost functional is regularized with two different multiplicative regularizers. The first regularizer is the weighted L(2)-norm total variation which provides an edge-preserving regularization. The second one imposes a priori information about the permittivities of the objects being imaged. Using both synthetically and experimentally collected data sets, we show that the proposed algorithm is robust in reconstructing the shape and location of homogeneous targets.
引用
收藏
页码：4790 / 4802
页数：13
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