TY - JOUR T1 - Compressive Covariance Sensing: Structure-Based Compressive Sensing Beyond Sparsity JF - IEEE Signal Processing Magazine Y1 - 2016 A1 - Daniel Romero A1 - Dyonisius D Ariananda A1 - Zhi Tian A1 - Geert Leus KW - compass KW - compressed sensing AB -
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.
VL - 33 IS - 1 ER -