Greedy pursuits for compressed sensing of jointly sparse signals

For compressed sensing with jointly sparse signals, we present a new signal model and two new joint iterative-greedy-pursuit recovery algorithms. The signal model is based on the assumption of a jointly shared support-set and the joint recovery algorithms have knowledge of the size of the shared support-set. Through experimental evaluation, we show that the new joint algorithms provide significant performance improvements compared to regular algorithms which do not exploit a joint sparsity.