A hybrid inversion scheme for through-the-wall imaging is proposed in this paper. The approach is based on a linearized model of the inverse-scattering problem, employing the Green’s function developed for a layered background. The reconstruction is obtained by means of a Landweberlike iterative method performing a regularization in the framework of variable-exponent Lebesgue spaces. Thanks to the non-conventional geometrical properties of such spaces, it is possible to enhance the reconstruction capabilities, e.g., by promoting sparseness and reducing over-smoothing. The exponent function defining the specific space adopted in the inversion procedure is adaptively obtained directly from the measured data, through a truncated-singular value decomposition method. In this way, it is possible to precompute and reuse in both steps, for a given scenario, all the matrices necessary in the inversion process, thus leading to a computationally efficient solving strategy. The effectiveness of the approach is evaluated by using experimental data obtained with a commercial GPR apparatus employing a pulsed source field. A fast Fourier transform is applied to the time-domain measurements to extract frequency-domain data at a set of frequencies in the source spectrum, which are fed in input to the imaging scheme. Very good reconstruction capabilities are obtained both with a single metallic target, as well as in a challenging two targets layout including both a metallic object and a low-permittivity target
Randazzo, A., Ponti, C., Fedeli, A., Estatico, C., D'atanasio, P., Pastorino, M., et al. (2021). A through-the-wall imaging approach based on a tsvd/variable-exponent lebesgue-space method. REMOTE SENSING, 13(11), 2028.
|Titolo:||A through-the-wall imaging approach based on a tsvd/variable-exponent lebesgue-space method|
|Data di pubblicazione:||2021|
|Citazione:||Randazzo, A., Ponti, C., Fedeli, A., Estatico, C., D'atanasio, P., Pastorino, M., et al. (2021). A through-the-wall imaging approach based on a tsvd/variable-exponent lebesgue-space method. REMOTE SENSING, 13(11), 2028.|
|Appare nelle tipologie:||1.1 Articolo in rivista|