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 language | English |
|---|---|
| Pages (from-to) | 2016-2025 |
| Number of pages | 10 |
| Journal | IEEE Transactions on Antennas and Propagation |
| Volume | 66 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2018 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- ?nite difference frequency domain (FDFD)
- Cross validation (CV)
- generalized multiple measurement vectors (GMMV)
- joint sparsity
- linear sampling method (LSM)
- transverse magnetic (TM)