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In this article, a unified learning-based approach is introduced to solve inverse scattering problems (ISPs) with mixed boundary conditions (BCs). The scattering behavior of hybrid dielectric and perfect electric conductors (PEC) scatterers is modeled by the T-matrix method. A rough image of the zero-order T-matrix coefficients for unknown scatterers is first reconstructed by the backpropagation (BP) method, which is then refined by an attention-assisted pix2pix generative adversarial network (GAN). The spatial attention mechanism is utilized to enforce the generator network to learn salient features of the unknown scatterers instead of the background. The adversarial training of the generator and the discriminator further enables the reconstructed image to be constrained by high-level features of reference scatterers. Numerical tests on both synthetic and experimental data verify the superior performance of the proposed method for ISP reconstructions with hybrid scatterers. It effectively expands the application scope of learning-based ISP methods to reconstruct scatterers without knowing the BCs of scatterers in advance. [ABSTRACT FROM AUTHOR]

In this study, scattering from impedance bodies positioned at the edge of a perfectly electrically conducting wedge is investigated. In the treatment of the problem, eigenfunction expansion in terms of spherical vector wave functions is employed. A complete dyadic Green's function for the spherical impedance boss at the edge is developed. It is observed that the scattering is highly enhanced by the edge guided waves. Additionally, using T-matrix method, the solution is extended to the general case of irregularly shaped scatterers. The T-matrix solution is verified by applying it to the case of a spherical scatterer and results are compared with the dyadic Green's function solution. [ABSTRACT FROM PUBLISHER]