  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 signal
matrix are temporally correlated. With signal's temporal correlation in mind,
we provide a framework of iterative MMV algorithms based on thresholding,
functional feedback and null space tuning. Convergence analysis for exact
recovery is established. Unlike most of iterative greedy algorithms that select
indices in a measurement/solution space, we determine indices based on an
orthogonal subspace spanned by the iterative sequence. In addition, a
functional feedback that controls the amount of energy relocation from the
"tails" is implemented and analyzed. It is seen that the principle of
functional feedback is capable to lower the number of iteration and speed up
the convergence of the algorithm. Numerical experiments demonstrate that the
proposed algorithm has a clearly advantageous balance of efficiency, adaptivity
and accuracy compared with other state-of-the-art algorithms.
