MMV Subspace Pursuit (M-SP) Algorithm for Joint Sparse Multiple Measurement Vectors Recovery

Abstract

In this paper, MMV Subspace Pursuit (M-SP) algorithm is proposed for solving joint sparse multiple measurement vectors (MMV) problem. The pre-selection and backtracking mechanisms are used in M-SP, so M-SP not only has higher recovery performance than some existing algorithms, but also significantly reduces the iteration number for improving the signal recovery efficiency. Simulations results show that M-SP and Simultaneous Compressive Sampling Matching Pursuit (SCoSaMP) have almost identical recovery performance and iteration times, but M-SP significantly reduces the computation complexity in per iteration. For example, when sparsity $K$ is 5, the computational complexity of M-SP is 24.0% of that of SCoSaMP in each iteration.