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

Journal article (2018)

Authors

T. Jin National University of Defense Technology

DOI: 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)

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Published Date

2018

Language

English

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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 Maxwell’s 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).

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