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In this work, combined calibration and DoA estimation is approached as anextension of the formulation for the Single Measurement Vector (SMV) model ofself-calibration to the Multiple Measurement Model (MMV) case. By takingadvantage of multiple snapshots, a modified nuclear norm minimization problemis proposed to recover a low-rank larger dimension matrix. We also give thedefinition of a linear operator for the MMV model, and give its correspondingmatrix representation to generate a variant of a convex optimization problem.In order to mitigate the computational complexity of the approach, singularvalue decomposition (SVD) is applied to reduce the problem size. Theperformance of the proposed methods are demonstrated by numerical simulations.

Low Rank Matrix Recovery for Joint Array Self-Calibration and Sparse Model DoA Estimation