Sparse signal recovery in the presence of correlated multiple measurement vectors

Sparse signal recovery algorithms utilizing multiple measurement vectors (MMVs) are known to have better performance compared to the single measurement vector case. However, current work rarely consider the case when sources have temporal correlation, a likely situation in practice. In this work we examine methods to account for temporal correlation and its impact on performance. We model sources as AR processes, and then incorporate such information into the framework of sparse Bayesian learning for sparse signal recovery. Experiments demonstrate the superiority of the proposed algorithms. They also show that the performance of existing algorithms are limited by temporal correlation, and that if such correlation can be fully exploited, as in our proposed algorithms, the limitation can be overcome.