Joint-Sparse Signal Reconstruction Based on Common Support Set Refinement

Joint-sparse signal reconstruction is a key issue in distributed compressed sensing based on the mixed support set model. In this letter, a novel joint-sparse signal reconstruction algorithm is proposed based on the common support set refinement. The common support set is first roughly estimated by greedy pursuit algorithms. The roughly estimated common support set is then refined by pruning the incorrect elements. With the refined common support set, greedy pursuit is utilized to reconstruct the joint-sparse signals. The complexity analysis and simulation results indicate that the proposed algorithm achieves better estimates of the support sets and finally reduces the reconstruction error for the joint-sparse signals with a moderate complexity compared with the state-of-the-art algorithms.