  In this work, combined calibration and DoA estimation is approached as an
extension of the formulation for the Single Measurement Vector (SMV) model of
self-calibration to the Multiple Measurement Model (MMV) case. By taking
advantage of multiple snapshots, a modified nuclear norm minimization problem
is proposed to recover a low-rank larger dimension matrix. We also give the
definition of a linear operator for the MMV model, and give its corresponding
matrix representation to generate a variant of a convex optimization problem.
In order to mitigate the computational complexity of the approach, singular
value decomposition (SVD) is applied to reduce the problem size. The
performance of the proposed methods are demonstrated by numerical simulations.
