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An iterative multiscaling approach for solving the electromagnetic inverse scattering problem related to the imaging of shallow subsurface targets with the ground-penetrating radar (GPR) is proposed. The approach combines the zooming properties of the multiscaling technique with the reconstruction capabilities of an Inexact-Newton (IN) method developed in $L^{p}$ spaces. It is based on multifrequency processing that allows one to face the ill-posedness of the inverse scattering problem by exploiting the regularization properties of a truncated Landweber (LW) method. Experimental data, extracted from radargrams obtained by the GPR in a real situation, are used for validation. The reconstruction results are also compared with those from competitive alternatives, such as a standard IN method or a state-of-the-art multifrequency Conjugate Gradient (CG)-based approach. [ABSTRACT FROM AUTHOR]

A conjugate-gradient-based regularization technique in $L^{p}$ Banach spaces, in conjunction with an inexact-Newton solving scheme, is presented in this paper. The proposed method is applied to obtain the reconstruction of the dielectric properties of human tissues in hemorrhagic brain stroke microwave imaging. The dielectric reconstruction is obtained by solving a nonlinear inverse problem, starting from a set of scattered-field measurements. The presented numerical simulations with 2-D and 3-D anatomically realistic phantoms, as well as the preliminary experimental results, show that this method can be useful for retrieving the dielectric properties of the human head with a reduced over-smoothing effect in the final images. [ABSTRACT FROM AUTHOR]

A method for microwave imaging of dielectric targets is proposed. It is based on a tomographic approach in which the field scattered by an unknown target (and collected in a proper observation domain) is inverted by using an inexact-Newton method developed in Lp Banach spaces. In particular, the extension of the approach to multifrequency data processing is reported. The mathematical formulation of the new method is described and the results of numerical simulations are reported and discussed, analyzing the behavior of the multifrequency processing technique combined with the Banach spaces reconstruction method. [ABSTRACT FROM AUTHOR]

An electromagnetic inverse scattering approach for imaging of shallow subsurface objects is reported. It extends to multifrequency processing, an efficient method previously developed for single frequency imaging. The considered approach is an iterative procedure based on an inexact-Newton method developed in L^p Banach spaces, which exhibits effective regularization capabilities and reduced over-smoothing effects. The approach is validated using numerical simulations in which cylindrical scatterers are reconstructed in a homogeneous lossy medium. Results are compared with those obtained using standard single frequency operating conditions. [ABSTRACT FROM PUBLISHER]

A new regularisation scheme is proposed for the solution of the inverse scattering problem arising in electromagnetic imaging. The aim is to efficiently reconstruct defects and cracks with sharp boundaries when inspecting civil structures. The approach is developed in Banach spaces and tends to alleviate the over-smoothing effects of the usually adopted regularisation schemes in Hilbert spaces. Preliminary numerical results concerning the tomographic reconstruction of defects in cylindrical models made of cement paste are reported and discussed. The improved reconstruction quality (in terms of the reduction in the smoothing effect) is proven in those cases. [ABSTRACT FROM AUTHOR]

In principle, the dielectric reconstruction of structures with a multi-layer configuration can have several practical implications. Layered scenarios are indeed present in a quite large number of circumstances, from geophysical inspections [1] to road pavements evaluation [2]. In all these contexts, the ground-penetrating radar (GPR) is a valuable instrument to acquire the back-scattered electromagnetic signals arising from buried targets and discontinuities. However, starting from GPR data, the retrieval of geometric and dielectric properties of such structures is not a straightforward task and is frequently left to operator's interpretation skills or accomplished in qualitative ways. Conversely, the possibility of an accurate characterization of the dielectric properties of objects buried in multi-layer environments may represent a further step toward their automated recognition and classification. The main obstacle in this direction is the need to resolve an ill-posed nonlinear inverse scattering problem, which requires advanced processing techniques with proper regularization capabilities. In free-space and half-space configurations, promising results have been obtained by adopting Newton-type inversion methods in Banach spaces, in particular with multi-frequency formulations [3]. This kind of technique is extended in order to reconstruct targets in three-layer scenarios. Such an extension includes changes in the kernels of the adopted integral operators, which reflect in an increased complexity of the relationship between scattered-field data and target properties. Initial numerical simulations are proposed to validate the inversion procedure and evaluate its performance in a fully controlled environment.[1] I. Catapano, A. Affinito, A. D. Moro, G. Alli, and F. Soldovieri, "Forward-looking ground-penetrating radar via a linear inverse scattering approach," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 10, pp. 5624–5633, Oct. 2015.[2] F. Tosti, L. Bianchini Ciampoli, F. D'Amico, A. M. Alani, and A. Benedetto, "An experimental-based model for the assessment of the mechanical properties of road pavements using ground-penetrating radar," Constr. Build. Mater., vol. 165, pp. 966–974, Mar. 2018.[3] C. Estatico, A. Fedeli, M. Pastorino, and A. Randazzo, "A multifrequency inexact-Newton method in Lp Banach spaces for buried objects detection," IEEE Trans. Antennas Propag., vol. 63, no. 9, pp. 4198–4204, Sep. 2015. [ABSTRACT FROM AUTHOR]