Sparse Signal Recovery in the Presence of Intra-Vector and Inter-Vector Correlation
This work addresses signal processing challenges in applications like communications or imaging, but it appears incremental as it builds on existing models and methods.
The paper tackles sparse signal recovery with correlated non-zero entries, analyzing intra-vector and inter-vector correlations in block sparse and multiple measurement vector models, and presents algorithms based on sparse Bayesian learning to incorporate these correlations.
This work discusses the problem of sparse signal recovery when there is correlation among the values of non-zero entries. We examine intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector model, as well as their combination. Algorithms based on the sparse Bayesian learning are presented and the benefits of incorporating correlation at the algorithm level are discussed. The impact of correlation on the limits of support recovery is also discussed highlighting the different impact intra-vector and inter-vector correlations have on such limits.