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Journal Articles Inverse Problems Year : 2012

A non-iterative sampling approach using noise subspace projection for EIT

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Abstract

This study concerns the problem of the reconstruction of inclusions embedded in a conductive medium in the context of electrical impedance tomography (EIT), which is investigated within the framework of a non-iterative sampling approach. This type of identification strategy relies on the construction of a special indicator function that takes, roughly speaking, small values outside the inclusion and large values inside. Such a function is constructed in this paper from the projection of a fundamental singular solution onto the space spanned by the singular vectors associated with some of the smallest singular values of the data-to-measurement operator. The behavior of the novel indicator function is analyzed. For a subsequent implementation in a discrete setting, the quality of classical finite-dimensional approximations of the measurement operator is discussed. The robustness of this approach is also analyzed when only noisy spectral information is available. Finally, this identification method is implemented numerically and experimentally, and its efficiency is discussed on a set of, partly experimental, examples.
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Dates and versions

hal-00757299 , version 1 (26-11-2012)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Cédric Bellis, Andrei Constantinescu, Thomas Coquet, Thomas Jaravel, Armin Lechleiter. A non-iterative sampling approach using noise subspace projection for EIT. Inverse Problems, 2012, ⟨10.1088/0266-5611/28/7/075015⟩. ⟨hal-00757299⟩
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