Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity

D Romero, D.D. Ariananda, Zhi Tian, G. Leus

Research output: Contribution to journalArticleScientificpeer-review

68 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)78-93
Number of pages16
JournalIEEE Signal Processing Magazine
Volume33
Issue number1
DOIs
Publication statusPublished - 2016

Keywords

  • Covariance matrices
  • Antenna arrays
  • Sensors
  • Image reconstruction
  • Compressed sensing

Fingerprint Dive into the research topics of 'Compressive Covariance Sensing: Structure-based compressive sensing beyond sparsity'. Together they form a unique fingerprint.

Cite this