Signal Processing With Compressive Measurements

The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been sh...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in signal processing 2010-04, Vol.4 (2), p.445-460
Main Authors: Davenport, M.A., Boufounos, P.T., Wakin, M.B., Baraniuk, R.G.
Format: Article
Language:English
Subjects:
Classification
Communities
Compressive sensing (CS)
compressive signal processing
Data mining
Design engineering
Digital signal processing
estimation
Filtering
Hardware
Pattern classification
Projection
Protocols
random projections
Recovery
Signal detection
Signal processing
Signal processing algorithms
Signal sampling
universal measurements
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The recently introduced theory of compressive sensing enables the recovery of sparse or compressible signals from a small set of nonadaptive, linear measurements. If properly chosen, the number of measurements can be much smaller than the number of Nyquist-rate samples. Interestingly, it has been shown that random projections are a near-optimal measurement scheme. This has inspired the design of hardware systems that directly implement random measurement protocols. However, despite the intense focus of the community on signal recovery, many (if not most) signal processing problems do not require full signal recovery. In this paper, we take some first steps in the direction of solving inference problems-such as detection, classification, or estimation-and filtering problems using only compressive measurements and without ever reconstructing the signals involved. We provide theoretical bounds along with experimental results.
ISSN:1932-4553
1941-0484
DOI:10.1109/JSTSP.2009.2039178