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

作者：Sun, Shilong^*; Kooij, Bert Jan; Yarovoy, Alexander G; Jin, Tian

来源：IEEE Transactions on Antennas and Propagation, 2018, 66(4): 2016-2025.

DOI：10.1109/TAP.2018.2806404

• Cross validation (CV)
• finite-difference frequency domain (FDFD)
• generalized multiple measurement vectors (GMMVs)
• joint sparsity
• linear sampling method (LSM)
• transverse magnetic (TM)

In this paper, a novel linear method for shape reconstruction is proposed based on the generalized multiple measurement vectors' (GMMVs) model. Finite-difference frequency domain is applied to discretize Maxwell's equations, and the contrast sources are solved iteratively by exploiting the joint sparsity as a regularized constraint. Cross validation 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 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.

• 出版日期2018-4
• 单位中国人民解放军国防科学技术大学