@article {825, title = {Compressive Covariance Sensing: Structure-Based Compressive Sensing Beyond Sparsity}, journal = {IEEE Signal Processing Magazine}, volume = {33}, year = {2016}, month = {01/2016}, pages = {78--93}, 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, the present
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.
}, keywords = {compass, compressed sensing}, author = {Daniel Romero and Dyonisius D Ariananda and Zhi Tian and Geert Leus} } @conference {762, title = {Cooperative compressive power spectrum estimation}, booktitle = {IEEE Sensor Array Multichannel Signal Process. Workshop (SAM)}, year = {2014}, keywords = {cognitive radio, wsn}, author = {Dyonisius D Ariananda and Daniel Romero and Geert Leus} }