A Linear Method for Shape Reconstruction based on the Generalized Multiple Measurement Vectors Model

Journal article (2018)

Authors

S. Sun Microwave Sensing, Signals & Systems - EEMCS
B.J. Kooij Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
T. Jin National University of Defense Technology
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2018 S. Sun, B.J. Kooij, Alexander Yarovoy, T. Jin
DOI
help_outline
https://doi.org/10.1109/TAP.2018.2806404
Transverse magnetic (TM) ?nite difference frequency domain (FDFD) Cross validation (CV) Generalized multiple measurement vectors (GMMV) Joint sparsity Linear sampling method (LSM)
More Info
expand_more

Published Date

2018

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

In this paper, a novel linear method for shape reconstruction is proposed based on the generalized multiple measurement vectors (GMMV) model. Finite difference frequency domain (FDFD) is applied to discretized Maxwells equations, and the contrast sources are solved iteratively by exploiting the joint sparsity as a regularized constraint. Cross validation (CV) technique is used to terminate the iterations, such that the required estimation of the noise level is circumvented. The validity is demonstrated with an excitation of transverse magnetic (TM) experimental data, and it is observed that, in the aspect of focusing performance, the GMMV-based linear method outperforms the extensively used linear sampling method (LSM).