Orthogonal Subspace Based Fast Iterative Thresholding Algorithms for Joint Sparsity Recovery

Sparse signal recoveries from multiple measurement vectors (MMV) with joint sparsity property have many applications in signal, image, and video processing. The problem becomes much more involved when snapshots of the matrix are temporally correlated. With signal's temporal correlation mind, we provide a framework iterative MMV algorithms based on thresholding, functional feedback null space tuning. Convergence analysis for exact recovery is established. Unlike most greedy that select indices measurement/solution space, determine an orthogonal subspace spanned by sequence. In addition, controls amount energy relocation “tails” implemented analyzed. It seen principle capable to lower number iteration speed up convergence algorithm. Numerical experiments demonstrate proposed algorithm has clearly advantageous balance efficiency, adaptivity accuracy compared other state-of-the-art algorithms.