Neural Network Approach for Wideband RCS Computation with Wide Incident Angles via Method of Moments

In this paper, we present a deep neural network–based approach for computing radar cross section (RCS) over a wide frequency band and a broad range of incident angles. The proposed network, termed WBRCS-Net, is designed to converge to the solution of the method of moments (MoM) formulation by minimizing a mean-squared residual loss without explicitly solving the MoM linear system, thereby avoiding the numerical instabilities commonly encountered in conventional iterative solvers. Moreover, by using only the frequency and incident angle as inputs, WBRCS-Net enables wideband RCS prediction over a broad range of incident angles while substantially simplifying the network architecture. The performance of WBRCS-Net is evaluated on perfectly electrically conducting (PEC) spheres and cubes and is compared with the Maehly approximation based on Chebyshev polynomials, using monostatic RCS over a frequency range of 2–12 GHz and an incident-angle range of 0∘∼90∘. Experimental results demonstrate that, once trained, WBRCS-Net enables stable wideband RCS computation over a wide range of incident angles with instantaneous inference speed, achieving a minimum mean-squared error (MSE) on the order of 10−14 relative to reference MoM solutions.