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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