Name
Abstract | ||
|---|---|---|
Compressed sensing deals with the reconstruction of signals from sub-Nyquist samples by exploiting the sparsity of their projections onto known subspaces. In contrast, this article is concerned with the reconstruction of second-order statistics, such as covariance and power spectrum, even in the absence of sparsity priors. The framework described here leverages the statistical structure of random processes to enable signal compression and offers an alternative perspective at sparsity-agnostic inference. Capitalizing on parsimonious representations, we illustrate how compression and reconstruction tasks can be addressed in popular applications such as power-spectrum estimation, incoherent imaging, direction-of-arrival estimation, frequency estimation, and wideband spectrum sensing. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/MSP.2015.2486805 | Signal Processing Magazine, IEEE |
Field | DocType | Volume |
Wideband,Pattern recognition,Computer science,Inference,Stochastic process,Spectral density,Artificial intelligence,Prior probability,Compressed sensing,Covariance,Signal compression | Journal | 33 |
Issue | ISSN | Citations |
1 | 1053-5888 | 25 |
PageRank | References | Authors |
0.84 | 32 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Romero | 1 | 25 | 0.84 |
| Dyonisius Dony Ariananda | 2 | 93 | 6.22 |
| Zhi Tian | 3 | 115 | 14.04 |
| G. Leus | 4 | 4344 | 307.24 |