EFSA: Physically Interpretable Facet Modeling for High-Frequency RCS Prediction

Learning physically interpretable and computationally efficient representations for high-frequency radar cross section (RCS) prediction is a critical yet unresolved challenge. While mesh-based Graph Neural Networks (GNNs) provide an excellent paradigm for understanding geometric and electromagnetic interactions, they are inherently bottlenecked by rigid sub-wavelength constraints, where shrinking wavelengths lead to catastrophic graph size expansion. To fundamentally decouple neural representations from these strict physical size requirements, we introduce the Equivalent Facet Scattering Amplitude (EFSA), which models the RCS contribution of facets at arbitrary scales. EFSA combines a Physical Optics (PO) prior with a learnable complex correction factor. Specifically, the PO prior absorbs the orders-of-magnitude variations in RCS contributions across differently scaled facets within the same target, while the correction factor captures the intra-facet coherent interference of facets significantly larger than the incident wavelength. Experiments on real-world targets demonstrate that our framework enables physically interpretable RCS prediction in high-frequency regimes while outperforming existing methods in predictive accuracy.