Name
Abstract | ||
|---|---|---|
Compressive sensing is a new methodology to capture signals at sub-Nyquist rate. To guarantee exact recovery from compressed measurements, one should choose specific matrix, which satisfies the Restricted Isometry Property (RIP), to implement the sensing procedure. In this letter, we propose to construct the sensing matrix with chaotic sequence following a trivial method and prove that with overwhelming probability, the RIP of this kind of matrix is guaranteed. Meanwhile, its experimental comparisons with Gaussian random matrix, Bernoulli random matrix and sparse matrix are carried out and show that the performances among these sensing matrix are almost equal. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/LSP.2010.2052243 | IEEE Signal Process. Lett. |
Keywords | Field | DocType |
overwhelming probability,signal sampling,chaos,chaotic sequence,compressive sensing,sensing matrix,matrix algebra,compressed measurements,trivial method,sub-nyquist rate,logistic map,probability,restricted isometry property,compressed sensing,sparse matrices,logistics,gaussian distribution,sampling methods,sun,random matrix,satisfiability,sparse matrix,strips,signal processing | Convergent matrix,Mathematical optimization,Generator matrix,Matrix (mathematics),Eigendecomposition of a matrix,State-transition matrix,Restricted isometry property,Mathematics,Sparse matrix,Random matrix | Journal |
Volume | Issue | ISSN |
17 | 8 | 1070-9908 |
Citations | PageRank | References |
6 | 0.56 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Yu | 1 | 50 | 13.27 |
| Jean Pierre Barbot | 2 | 21 | 1.87 |
| Gang Zheng | 3 | 109 | 19.51 |
| Hong Sun | 4 | 218 | 26.36 |