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

More Info
expand_more

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).

Files

43041349_TAP2806404.pdf
(pdf | 0.96 Mb)

Download not available

08292840.pdf
(pdf | 2.48 Mb)
- Embargo expired in 16-06-2021