Compressive sensing

Richard G. Baraniuk

doi:10.1109/MSP.2007.4286571

Compressive sensing

Richard G. Baraniuk

Research output: Contribution to journal › Article

3614 Scopus citations

Abstract

Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

Original language	English (US)
Journal	IEEE Signal Processing Magazine
Volume	24
Issue number	4
DOIs	https://doi.org/10.1109/MSP.2007.4286571
State	Published - 2007

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Access to Document

10.1109/MSP.2007.4286571

Fingerprint Dive into the research topics of 'Compressive sensing'. Together they form a unique fingerprint.

Cite this

@article{442e43ed34584462a9dc3d1a12e046ef,

title = "Compressive sensing",

abstract = "Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.",

author = "Baraniuk, {Richard G.}",

year = "2007",

doi = "10.1109/MSP.2007.4286571",

language = "English (US)",

volume = "24",

journal = "IEEE Signal Processing Magazine",

issn = "1053-5888",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

}

TY - JOUR

T1 - Compressive sensing

AU - Baraniuk, Richard G.

PY - 2007

Y1 - 2007

N2 - Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

AB - Signal acquisition based on compressive sensing can be more efficient than traditional sampling for sparse or compressible signals. This method captures and represents signals at a rate significantly below the Nyquist rate and employs nonadaptive linear projections preserving signal structure. Then, the signal is reconstructed using an optimization technique. The least squares optimization used is inadequate for signal reconstruction, and other types of convex optimization must be invoked.

UR - http://www.scopus.com/inward/record.url?scp=85032751965&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032751965&partnerID=8YFLogxK

U2 - 10.1109/MSP.2007.4286571

DO - 10.1109/MSP.2007.4286571

M3 - Article

AN - SCOPUS:85032751965

VL - 24

JO - IEEE Signal Processing Magazine

JF - IEEE Signal Processing Magazine

SN - 1053-5888

IS - 4

ER -