Adaptive compressed sensing for estimation of structured sparse sets

R.M. Castro, E.T. Tánczos

Research output: Book/ReportReportAcademic

1 Downloads (Pure)

Abstract

This paper investigates the problem of estimating the support of structured signals via adaptive compressive sensing. We examine several classes of structured support sets, and characterize the fundamental limits of accurately estimating such sets through compressive measurements, while simultaneously providing adaptive support recovery protocols that perform near optimally for these classes. We show that by adaptively designing the sensing matrix we can attain significant performance gains over non-adaptive protocols. These gains arise from the fact that adaptive sensing can: (i) better mitigate the effects of noise, and (ii) better capitalize on the structure of the support sets.
Original languageEnglish
Publishers.n.
Number of pages34
Publication statusPublished - 2014

Publication series

NamearXiv
Volume1410.4593 [math.ST]

Fingerprint Dive into the research topics of 'Adaptive compressed sensing for estimation of structured sparse sets'. Together they form a unique fingerprint.

Cite this