Compressive sampling for signal classification

Jarvis Haupt, Rui Castro, Robert Nowak, Gerald Fudge, Alex Yeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

103 Scopus citations


Compressive Sampling (CS), also called Compressed Sensing, entails making observations of an unknown signal by projecting it onto random vectors. Recent theoretical results show that if the signal is sparse (or nearly sparse) in some basis, then with high probability such observations essentially encode the salient information in the signal. Further, the signal can be reconstructed from these "random projections," even when the number of observations is far less than the ambient signal dimension. The provable success of CS for signal reconstruction motivates the study of its potential in other applications. This paper investigates the utility of CS projection observations for signal classification (more specifically, mary hypothesis testing). Theoretical error bounds are derived and verified with several simulations.

Original languageEnglish (US)
Title of host publicationConference Record of the 40th Asilomar Conference on Signals, Systems and Computers, ACSSC '06
Number of pages5
StatePublished - 2006
Event40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06 - Pacific Grove, CA, United States
Duration: Oct 29 2006Nov 1 2006

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393


Other40th Asilomar Conference on Signals, Systems, and Computers, ACSSC '06
Country/TerritoryUnited States
CityPacific Grove, CA


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