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| Content Provider | IET Digital Library |
|---|---|
| Author | Zhao, Guanghui Shen, Fangfang Wang, Zhengyang Shi, Guangming |
| Abstract | As a new enchanting theory, compressed sensing (CS) demonstrates that a sparse signal can be recovered through a surprisingly small number of linear measurements by solving a problem of ℓ 1 norm minimisation (which can be thought as a special case of the signomial diversity measures). However, the traditional CS model with ℓ 1 norm minimisation can not fully exploit the sparsity especially when the degree of sparsity increases or the measurements number reduces. In this study, the Cauchy diversity measures is incorporated into the proposed model to deal with the above difficulties. The simulation results demonstrate that under the same condition, this new model offers a superior reconstruction precision compared with the common used signomial diversity measures. |
| Starting Page | 791 |
| Ending Page | 799 |
| Page Count | 9 |
| ISSN | 17519675 |
| Volume Number | 7 |
| e-ISSN | 17519683 |
| Issue Number | Issue 9, Dec (2013) |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-spr/7/9 |
| Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-spr.2012.0329 |
| Journal | IET Signal Processing |
| Access Restriction | Open |
| Rights Holder | © The Institution of Engineering and Technology |
| Subject Keyword | Cauchy Diversity Measures Compressed Sensing CS Model Enchanting Theory Linear Measurement Minimisation Optimisation Technique Reconstruction Precision Signal Processing And Detection Signal Processing Theory Signal Reconstruction Signomial Diversity Measures Sparse Signal Recovery Sparsity Enhancement „“1 Norm Minimisation |
| Content Type | Text |
| Resource Type | Article |
| Subject | Signal Processing Electrical and Electronic Engineering |
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