Compressive distilled sensing : sparse recovery using adaptivity in compressive measurements

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

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

    62 Citations (Scopus)
    207 Downloads (Pure)

    Abstract

    The recently-proposed theory of distilled sensing establishes that adaptivity in sampling can dramatically improve the performance of sparse recovery in noisy settings. In particular, it is now known that adaptive point sampling enables the detection and/or support recovery of sparse signals that are otherwise too weak to be recovered using any method based on non-adaptive point sampling. In this paper the theory of distilled sensing is extended to highly-undersampled regimes, as in compressive sensing. A simple adaptive sampling-and-refinement procedure called compressive distilled sensing is proposed, where each step of the procedure utilizes information from previous observations to focus subsequent measurements into the proper signal subspace, resulting in a significant improvement in effective measurement SNR on the signal subspace. As a result, for the same budget of sensing resources, compressive distilled sensing can result in significantly improved error bounds compared to those for traditional compressive sensing.
    Original languageEnglish
    Title of host publicationProceedings of the 43th Annual Asilomar Conference on Signal, Systems and Computers (Pacific Grove CA, USA, November 1-4, 2009)
    PublisherInstitute of Electrical and Electronics Engineers
    Pages1551-1555
    ISBN (Print)978-1-4244-5825-7
    DOIs
    Publication statusPublished - 2009

    Fingerprint

    Dive into the research topics of 'Compressive distilled sensing : sparse recovery using adaptivity in compressive measurements'. Together they form a unique fingerprint.

    Cite this