Name
Abstract | ||
|---|---|---|
This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/LSP.2014.2349904 | Signal Processing Letters, IEEE |
Keywords | DocType | Volume |
distributed compressed sensing,off-the-grid formulation,common frequency-sparse component,frequency-sparse signal ensemble joint recovery,dual certificate characterization,mathematical programming,basis mismatch,compressed sensing,semidefinite program,joint sparsity,compressed measurement,concatenated atomic norm minimization approach,atomic norm,minimisation | Journal | 22 |
Issue | ISSN | Citations |
1 | 1070-9908 | 4 |
PageRank | References | Authors |
0.44 | 24 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenqi Lu | 1 | 11 | 2.02 |
| Rendong Ying | 2 | 75 | 19.11 |
| Sumxin Jiang | 3 | 7 | 2.87 |
| Zenghui Zhang | 4 | 50 | 10.29 |
| Pei-Lin Liu | 5 | 231 | 44.49 |
| Wenxian Yu | 6 | 329 | 43.89 |