Adaptive sensing for sparse signal recovery

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

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

    57 Citations (Scopus)
    178 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.
    Original languageEnglish
    Title of host publicationProceedings 13th IEEE Digital Signal Processing Workshop (DSP'09, Marco Island FL, USA, January 4-7, 2009)
    PublisherInstitute of Electrical and Electronics Engineers
    ISBN (Print)978-1-4244-3677-4
    Publication statusPublished - 2009


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