  We propose a novel sparsity model for distributed compressed sensing in the
multiple measurement vectors (MMV) setting. Our model extends the concept of
row-sparsity to allow more general types of structured sparsity arising in a
variety of applications like, e.g., seismic exploration and non-destructive
testing. To reconstruct structured data from observed measurements, we derive a
non-convex but well-conditioned LASSO-type functional. By exploiting the
convex-concave geometry of the functional, we design a projected gradient
descent algorithm and show its effectiveness in extensive numerical
simulations, both on toy and real data.
