The objective of this thesis work is to detect and to characterize three-dimensional targets in a disordered medium, using electromagnetic excitations. This research domain is of great interest in many applications, such as subsoil probing, medical imaging, non-destructive testing and geophysical exploration, etc. In order to extract the target information from the heterogeneities of the medium, we propose to use one of the time reversal technique, namely the DORT method (French acronym for Décomposition de l'Opérateur de Retournement Temporel). This method permits us to generate different waves that focus selectively on each target in high noisy environment. Moreover, this method is also combined with a non-linear inversion algorithm, which permits not only to localize but also to characterize the targets. The reconstruction resolution appears to be better than the ones obtained with the DORT or the inversion procedure alone, especially in the illumination direction. It is also shown that using full-polarized data is indispensable for achieving better performances rather than in scalar configuration. Moreover, in the half-space configuration, it is mandatory to use the frequency-diversity data to get an accurate reconstruction. These theoretical developments are also confronted to experimental data measured in the optical domain. A full-polarization Tomographic Diffractive Microscopy (TDM) is implemented and a resolution about one-fourth of the wavelength is thus obtained. Furthermore, the DORT method is applied in TDM to realize selective focalization and characterization. In the presence of multiple targets, selective characterization of each scatterer is achieved.This thesis work also deals with the characterization problem using transient data. Different inversion algorithms are validated using synthetic and experimental hyper-frequency data.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-01037906 |
| Date | 03 March 2014 |
| Creators | Zhang, Ting |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | fra |
| Detected Language | English |
| Type | PhD thesis |
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