A reduced-order-model Bayesian obstacle detection algorithm. (English) Zbl 1448.62020
Wood, David R. (ed.) et al., 2018 MATRIX annals. Cham: Springer. MATRIX Book Ser. 3, 17-27 (2020).
Summary: 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.
For the entire collection see [Zbl 1445.37004].
For the entire collection see [Zbl 1445.37004].
MSC:
| 62A01 | Foundations and philosophical topics in statistics |
| 62H30 | Classification and discrimination; cluster analysis (statistical aspects) |
| 41A10 | Approximation by polynomials |
Keywords:
generalised polynomial chaos approximation; classifying two-dimensional objects; wave-propagationSoftware:
TMATROMReferences:
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