Compressive covariance sampling

Daniel Romero, Geert Leus

Research output: Contribution to conferencePaperpeer-review

34 Scopus citations


Most research efforts in the field of compressed sensing have been pointed towards analyzing sampling and reconstruction techniques for sparse signals, where sampling rates below the Nyquist rate can be reached. When only second-order statistics or, equivalently, covariance information is of interest, perfect signal reconstruction is not required and rate reductions can be achieved even for non-sparse signals. This is what we will refer to as compressive covariance sampling. In this paper, we will study minimum-rate compressive covariance sampling designs within the class of non-uniform samplers. Necessary and sufficient conditions for perfect covariance reconstruction will be provided and connections to the well-known sparse ruler problem will be highlighted.

Original languageEnglish (US)
Number of pages8
StatePublished - 2013
Event2013 Information Theory and Applications Workshop, ITA 2013 - San Diego, CA, United States
Duration: Feb 10 2013Feb 15 2013


Other2013 Information Theory and Applications Workshop, ITA 2013
Country/TerritoryUnited States
CitySan Diego, CA


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