NDLI: Structured Compressed Sensing: From Theory to Applications

Content Provider	IEEE Xplore Digital Library
Author	Duarte, M.F. Eldar, Y.C.
Copyright Year	1991
Abstract	Compressed sensing (CS) is an emerging field that has attracted considerable research interest over the past few years. Previous review articles in CS limit their scope to standard discrete-to-discrete measurement architectures using matrices of randomized nature and signal models based on standard sparsity. In recent years, CS has worked its way into several new application areas. This, in turn, necessitates a fresh look on many of the basics of CS. The random matrix measurement operator must be replaced by more structured sensing architectures that correspond to the characteristics of feasible acquisition hardware. The standard sparsity prior has to be extended to include a much richer class of signals and to encode broader data models, including continuous-time signals. In our overview, the theme is exploiting signal and measurement structure in compressive sensing. The prime focus is bridging theory and practice; that is, to pinpoint the potential of structured CS strategies to emerge from the math to the hardware. Our summary highlights new directions as well as relations to more traditional CS, with the hope of serving both as a review to practitioners wanting to join this emerging field, and as a reference for researchers that attempts to put some of the existing ideas in perspective of practical applications.
Sponsorship	IEEE Signal Processing Society
Starting Page	4053
Ending Page	4085
Page Count	33
File Size	2035405
File Format	PDF
ISSN	1053587X
Volume Number	59
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hardware Sparks Coherence Digital signal processing Sensors Vectors Compressed sensing sampling methods Approximation algorithms compressed sensing compression algorithms data acquisition data compression
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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