Adaptive sensing for sparse signal recovery

J. Haupt, R. Nowak, R.M. Castro

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

    47 Citaten (Scopus)
    93 Downloads (Pure)


    The theory of compressed sensing shows that sparse signals in high-dimensional spaces can be recovered from a relatively small number of samples in the form of random projections. However, in severely resource-constrained settings even CS techniques may fail, and thus, a less aggressive goal of partial signal recovery is reasonable. This paper describes a simple data-adaptive procedure that efficiently utilizes information from previous observations to focus subsequent measurements into subspaces that are increasingly likely to contain true signal components. The procedure is analyzed in a simple setting, and more generally, shown experimentally to be more effective than methods based on traditional (non-adaptive) random projections for partial signal recovery.
    Originele taal-2Engels
    TitelProceedings 13th IEEE Digital Signal Processing Workshop (DSP'09, Marco Island FL, USA, January 4-7, 2009)
    UitgeverijInstitute of Electrical and Electronics Engineers
    ISBN van geprinte versie978-1-4244-3677-4
    StatusGepubliceerd - 2009

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