TitleCompressive Covariance Sensing: Structure-Based Compressive Sensing Beyond Sparsity
Publication TypeJournal Article
Year of Publication2016
AuthorsRomero, D, Ariananda, DD, Tian, Z, Leus, G
JournalIEEE Signal Processing Magazine
Date Published01/2016
Keywordscompass, compressed sensing
Abstract Compressed sensing deals with the reconstruction of signalsfrom sub-Nyquist samples by exploiting the sparsity of theirprojections onto known subspaces. In contrast, the presentarticle is concerned with the reconstruction of second-orderstatistics, such as covariance and power spectrum, even inthe absence of sparsity priors. The framework described hereleverages the statistical structure of random processes toenable signal compression and offers an alternative perspectiveat sparsity-agnostic inference. Capitalizing on parsimoniousrepresentations, we illustrate how compression and reconstructiontasks can be addressed in popular applications suchas power spectrum estimation, incoherent imaging, directionof arrival estimation, frequency estimation, and widebandspectrum sensing.