A reduced-order-model Bayesian obstacle detection algorithm

Mahadevan Ganesh, Stuart C. Hawkins

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


    We develop an efficient Bayesian algorithm for solving the inverse problem of classifying and locating certain two dimensional objects using noisy far field data obtained by illuminating them with a radiating wave. While application of Bayesian algorithms for wave-propagation inverse problems is itself innovative, the principal novelty in this work is in using (i) a surrogate Bayesian posterior distribution computed using a generalised polynomial chaos approximation; and (ii) an efficient wave-propagation-specific reduced order model in place of the full multiple scattering forward model. We demonstrate the capability of this approach with simulations in which we accurately detect two dimensional objects, with shapes motivated by safety and security applications.
    Original languageEnglish
    Title of host publication2018 Matrix annals
    EditorsDavid R. Wood, Jan de Gier, Cheryl E. Praeger, Terence Tao
    Place of PublicationCham
    PublisherSpringer, Springer Nature
    Number of pages11
    ISBN (Electronic)9783030382308
    ISBN (Print)9783030382292
    Publication statusPublished - 2020
    Event2018 Matrix Annals - , Australia
    Duration: 8 Jan 201821 Dec 2018

    Publication series

    NameMATRIX Book Series
    ISSN (Print)2523-3041
    ISSN (Electronic)2523-305X


    Conference2018 Matrix Annals


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