  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).
