期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 62, 期 7, 页码 1694-1704出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2014.2301980
关键词
Compressed sensing; greedy algorithms; multiple measurement vectors; joint sparsity; row sparse matrices; performance comparison
资金
- Grinnell College MAP Program
- [NSF DMS 11126152]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1112612] Funding Source: National Science Foundation
Five known greedy algorithms designed for the single measurement vector setting in compressed sensing and sparse approximation are extended to the multiple measurement vector scenario: Iterative Hard Thresholding (IHT), Normalized IHT (NIHT), Hard Thresholding Pursuit (HTP), Normalized HTP (NHTP), and Compressive Sampling Matching Pursuit (CoSaMP). Using the asymmetric restricted isometry property (ARIP), sufficient conditions for all five algorithms establish bounds on the discrepancy between the algorithms' output and the optimal row-sparse representation. When the initial multiple measurement vectors are jointly sparse, ARIP-based guarantees for exact recovery are also established. The algorithms are then compared via the recovery phase transition framework. The strong phase transitions describing the family of Gaussian matrices which satisfy the sufficient conditions are obtained via known bounds on the ARIP constants. The algorithms' empirical weak phase transitions are compared for various numbers of multiple measurement vectors. Finally, the performance of the algorithms is compared against a known rank aware greedy algorithm, Rank Aware Simultaneous Orthogonal Matching Pursuit + MUSIC. Simultaneous recovery variants of NIHT, NHTP, and CoSaMP all outperform the rank-aware algorithm.
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