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

Shilong Sun, Bert Jan Kooij, Alexander G. Yarovoy, Tian Jin

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
16 Downloads (Pure)

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).

Original languageEnglish
Pages (from-to)2016-2025
Number of pages10
JournalIEEE Transactions on Antennas and Propagation
Volume66
Issue number4
DOIs
Publication statusPublished - 2018

Keywords

  • ?nite difference frequency domain (FDFD)
  • Cross validation (CV)
  • generalized multiple measurement vectors (GMMV)
  • joint sparsity
  • linear sampling method (LSM)
  • transverse magnetic (TM)

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