Tomographic Imaging of Sparse Low-Contrast Targets in Harsh Environments Through Matrix Completion

Abstract : A new methodology that exploits the matrix completion (MC) paradigm is proposed to image weak and sparse scatterers in heavy noise conditions. The 2-D inverse problem, mathematically formulated under the first-order Born approximation, is addressed with a three-phase algorithm that consists of: 1) an initial estimation step where a preliminary reconstruction of the distribution of the contrast and the associated “confidence map” are computed by means of a Bayesian compressive sensing method; 2) a filtering step devoted to identify and discard the less reliable contrast coefficients; and 3) a final dielectric profile completion step aimed at recovering a faithful image of the whole scattering scenario by exploiting a customized MC procedure. Representative numerical results and comparisons with competitive state-of-the-art inversion techniques are reported and discussed to assess the accuracy, the robustness, and the numerical efficiency of the proposed approach.
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Article dans une revue
IEEE Transactions on Microwave Theory and Techniques, Institute of Electrical and Electronics Engineers, 2018, 66 (6), pp.2714-2730. 〈10.1109/TMTT.2018.2825393〉
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Contributeur : Andrea Massa <>
Soumis le : jeudi 7 juin 2018 - 10:59:50
Dernière modification le : vendredi 15 juin 2018 - 16:36:02

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Giacomo Oliveri, Marco Salucci, Nicola Anselmi. Tomographic Imaging of Sparse Low-Contrast Targets in Harsh Environments Through Matrix Completion. IEEE Transactions on Microwave Theory and Techniques, Institute of Electrical and Electronics Engineers, 2018, 66 (6), pp.2714-2730. 〈10.1109/TMTT.2018.2825393〉. 〈hal-01809840〉

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