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Blind deconvolution via independent component analysis for thin-pavement thickness estimation using GPR
Within the scope of GPR application to pavement survey, the sparse reflectivity series representing the layered structure of the pavement is convolved with the radar wavelet. A successful blind deconvolution would then retrieve the latter reflectivity series and improves the time resolution without any a priori information. In this paper, we cast the convolutional model as a multidimensional data model which renders blind deconvolution via independent component analysis (ICA) possible. We use a nonlinear contrast function which is matched to the sparse nature of the reflectivity series. The method is tested on synthetic and real GPR data from a thin PVC slab. The results attest to the accuracy of the time delay estimates and verify the high resolution of the proposed approach.
Blind deconvolution via independent component analysis for thin-pavement thickness estimation using GPR
Within the scope of GPR application to pavement survey, the sparse reflectivity series representing the layered structure of the pavement is convolved with the radar wavelet. A successful blind deconvolution would then retrieve the latter reflectivity series and improves the time resolution without any a priori information. In this paper, we cast the convolutional model as a multidimensional data model which renders blind deconvolution via independent component analysis (ICA) possible. We use a nonlinear contrast function which is matched to the sparse nature of the reflectivity series. The method is tested on synthetic and real GPR data from a thin PVC slab. The results attest to the accuracy of the time delay estimates and verify the high resolution of the proposed approach.
Blind deconvolution via independent component analysis for thin-pavement thickness estimation using GPR
Unbekannte Entfaltung im Vergleich mit unabhängiger Komponentenanalyse
Chahine, Khaled (Autor:in) / Baltazart, Vincent (Autor:in) / Wang, Yide (Autor:in) / Derobert, Xavier (Autor:in)
2009
6 Seiten, 5 Bilder, 11 Quellen
(nicht paginiert)
Aufsatz (Konferenz)
Datenträger
Englisch
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