Texas hold 'em algorithms for distributed compressive sensing

Stephen R. Schnelle, Jason N. Laska, Chinmay Hegde, Marco F. Duarte, Mark A. Davenport, Richard G. Baraniuk

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

    Abstract

    This paper develops a new class of algorithms for signal recovery in the distributed compressive sensing (DCS) framework. DCS exploits both intra-signal and inter-signal correlations through the concept of joint sparsity to further reduce the number of measurements required for recovery. DCS is well-suited for sensor network applications due to its universality, computational asymmetry, tolerance to quantization and noise, and robustness to measurement loss. In this paper we propose recovery algorithms for the sparse common and innovation joint sparsity model. Our approach leads to a class of efficient algorithms, the Texas Hold 'Em algorithms, which are scalable both in terms of communication bandwidth and computational complexity.

    Original languageEnglish (US)
    Title of host publication2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Proceedings
    Pages2886-2889
    Number of pages4
    DOIs
    StatePublished - 2010
    Event2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010 - Dallas, TX, United States
    Duration: Mar 14 2010Mar 19 2010

    Publication series

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

    Other

    Other2010 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010
    CountryUnited States
    CityDallas, TX
    Period3/14/103/19/10

    Keywords

    • Data compression
    • Multisensor systems
    • Signal reconstruction

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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