Sparse Signal Recovery Based on Complex Bayesian Compressive Sensing

An effective Sparse Bayesian Learning algorithm exploiting Complex sparse Temporal correlation (CTSBL) is proposed in this paper, which is used to recover sparse complex signal. By exploiting the fact that the real and imaginary components of a complex value share the same sparsity pattern, it can improve the sparsity of the estimated signal. A multitask sparse signal recovery issue is transformed to a block sparse signal recovery issue of a single measurement by taking full advantage of the internal structure information among the multiple measurement vector signals. The experiments show that the proposed algorithm CTSBL achieves better recovery performance compared with the existing Complex MultiTask Bayesian Compressive Sensing (CMTBCS) algorithm and BOMP algorithm.