Name
Abstract | ||
|---|---|---|
This letter considers the problem of observation strategy design for compressed sensing. An adaptive method, based on Cramer-Rao bound minimization, is proposed to design the sensing matrix. Simulation results demonstrate that the adaptively constructed sensing matrix can lead to much lower recovery errors than those of traditional Gaussian matrices and some existing adaptive approaches. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/LSP.2014.2299814 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
adaptively constructed sensing matrix,cramer-rao bound minimization,cramer,gaussian matrices,adaptive compressed sensing,adaptive sampling,cramer–rao bound,adaptive signal processing,rao bound,matrix algebra,estimation theory,compressed sensing,recovery errors,subspace pursuit,minimisation,sparse matrices,vectors,sensors,signal to noise ratio,cramer rao bound | Cramér–Rao bound,Mathematical optimization,Pattern recognition,Matrix (mathematics),Minification,Gaussian,Minimisation (psychology),Adaptive filter,Artificial intelligence,Estimation theory,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
21 | 3 | 1070-9908 |
Citations | PageRank | References |
6 | 0.44 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianyao Huang | 1 | 79 | 10.86 |
| Yimin Liu | 2 | 158 | 25.46 |
| Huadong Meng | 3 | 175 | 20.65 |
| Xiqin Wang | 4 | 290 | 33.88 |