Block-Sparse Signals Recovery using Orthogonal Multimatching

Compressed Sensing is an efficient signal acquisition approach that projects input signals,embedded in a high-dimensional space,into signals that lie in a space of significantly smaller dimensions,and solves an optimization problem,then recovers the input signals from the projections.Block-spase signal is a typical sparse signal,the units in the same block can simultaneously tend to be zeros or nonzeros.As to the feature of block-sparse signal,an orthogonal multimatching pursuit algorithm(BOMMP) for block-sparse signals recovery has been proposed in this paper.The algorithm picks at least one correct index at each iteration,additionaly,the support set and the residual will be refined,finally,the recovery signal can be determined by the pseudo-inverse.The simulation results demonstrate that the recovery probability of BOMMP is higher than most existing algorithms.