Number of measurements in sparse signal recovery
We analyze the asymptotic performance of sparse signal recovery from noisy measurements. In particular, we generalize some of the existing results for the Gaussian case to sub-Gaussian and other ensembles. An achievable result is presented for the linear sparsity regime. A converse on the number of...
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| creator | Tune, P. Bhaskaran, S.R. Hanly, S. |
| description | We analyze the asymptotic performance of sparse signal recovery from noisy measurements. In particular, we generalize some of the existing results for the Gaussian case to sub-Gaussian and other ensembles. An achievable result is presented for the linear sparsity regime. A converse on the number of required measurements in the sub-linear regime is also presented, which cover many of the widely used measurement ensembles. Our converse idea makes use of a correspondence between compressed sensing ideas and compound channels in information theory. |
| doi_str_mv | 10.1109/ISIT.2009.5205809 |
| format | Conference Proceeding |
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In particular, we generalize some of the existing results for the Gaussian case to sub-Gaussian and other ensembles. An achievable result is presented for the linear sparsity regime. A converse on the number of required measurements in the sub-linear regime is also presented, which cover many of the widely used measurement ensembles. Our converse idea makes use of a correspondence between compressed sensing ideas and compound channels in information theory.</abstract><pub>IEEE</pub><doi>10.1109/ISIT.2009.5205809</doi><tpages>5</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2157-8095 |
| ispartof | 2009 IEEE International Symposium on Information Theory, 2009, p.16-20 |
| issn | 2157-8095 2157-8117 |
| language | eng |
| recordid | cdi_ieee_primary_5205809 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Australia Compressed sensing Error probability Information theory Particle measurements Performance analysis Random variables Signal analysis Sparse matrices Vectors |
| title | Number of measurements in sparse signal recovery |
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