Accurate, grid-robust and versatile combined-field discretization for the electromagnetic scattering analysis of perfectly conducting targets

Recent implementations of the Electric-Field Integral Equation (EFIE) for the electromagnetic scattering analysis of perfectly conducting targets rely on the electric current expansion with the monopolar-RWG basis functions, discontinuous across mesh edges, and the field testing over volumetric subdomains attached to the surface boundary triangulation. As compared to the standard RWG-based EFIE-approaches, normally continuous across edges, these schemes exhibit enhanced versatility, allowing the analysis of geometrically non-conformal meshes, and improved accuracy, especially for subwavelength sharp-edged conductors. In this paper, we present a monopolar-RWG discretization by the Method of Moments (MoM) of the Combined-Field Integral Equation (CFIE) resulting from the addition of a volumetrically tested discretization of the EFIE and the Galerkin tested MFIE-implementation. We show for sharp-edged conductors the degree of improved accuracy in the computed RCS and the convergence properties in the iterative search of the solution. More importantly, as we show in the paper, these implementations become in practice advantageous because of their robustness to flaws in the grid generation or their agility in handling complex meshes arising from the interconnection of independently meshed domains. The hybrid RWG/monopolar-RWG discretization of the CFIE defines the RWG discretization over geometrically conformal and smoothly varying mesh regions and inserts the monopolar-RWG expansion strictly at sharp edges, for improved accuracy purposes, or over boundary lines between partitioning mesh domains, for the sake of enhanced versatility. These hybrid schemes offer similar accuracy as their fully monopolar-RWG counterparts but with fewer unknowns and allow naturally non-conformal mesh transitions without inserting additional inter-domain continuity conditions or new artificial currents. (C) 2020 Elsevier Inc. All rights reserved.