Compressive Covariance Sensing

Structure-based compressive sensing beyond sparsity

Journal article (2016)

Authors

Daniel Romero University of Minnesota

D.D. Ariananda Signal Processing Systems -

Zhi Tian George Mason University

G.J.T. Leus Signal Processing Systems -

Research Group

Signal Processing Systems () (TU Delft)

Sensors Image reconstruction Compressed sensing Antenna arrays Covariance matrices

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http://resolver.tudelft.nl/uuid:3ec98bd4-1288-452f-a129-838391254e4e

Published Date

2016

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.