Compressive sampling for signal detection

Jarvis Haupt, Robert Nowak

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

106 Scopus citations

Abstract

Compressive sampling (CS) refers to a generalized sampling paradigm in which observations are inner products between an unknown signal vector and user-specified test vectors. Among the attractive features of CS is the ability to reconstruct any sparse (or nearly sparse) signal from a relatively small number of samples, even when the observations are corrupted by additive noise. However, the potential of CS in other signal processing applications is still not fully known. This paper examines the performance of CS for the problem of signal detection. A generalized restricted isometry property (GRIP) is introduced, which guarantees that angles are preserved, in addition to the usual norm preservadon, by CS. The GRIP is leveraged to derive error bounds for a CS matched filtering scheme, and to show that the scheme is robust to signal mismatch.

Original languageEnglish (US)
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
PagesIII1509-III1512
DOIs
StatePublished - Aug 6 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 - Honolulu, HI, United States
Duration: Apr 15 2007Apr 20 2007

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume3
ISSN (Print)1520-6149

Other

Other2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07
CountryUnited States
CityHonolulu, HI
Period4/15/074/20/07

Keywords

  • Signal detection

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