Compressive sampling for signal classification

J. Haupt, R.M. Castro, R. Nowak, G. Fudge, A. Yeh

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    94 Citations (Scopus)
    1 Downloads (Pure)

    Abstract

    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, m-ary hypothesis testing). Theoretical error bounds are derived and verified with several simulations.
    Original languageEnglish
    Title of host publicationProceedings 40th Asilomar Conference on Signals, Systems and Computers (ACSSC'06, Pacific Grove CA, USA, October 29-November 6, 2006)
    PublisherInstitute of Electrical and Electronics Engineers
    Pages1430-1434
    ISBN (Print)1-4244-0784-2
    DOIs
    Publication statusPublished - 2006

    Fingerprint Dive into the research topics of 'Compressive sampling for signal classification'. Together they form a unique fingerprint.

    Cite this