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eess.SP updates on arXiv.org

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Ray-Based Simulation of Scattering from Discretized Curve...
[Submitted on 7 Apr 2026 (v1), last revised 2 Jul 2026 (this ver · 2026-04-07 · via eess.SP updates on arXiv.org

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Abstract:Realistic modeling of scattering from curved metallic bodies - such as vehicles and roadside structures - is essential for cellular and vehicular channel modeling as well as radar applications. A practical approach is to approximate curved surfaces with planar facets and apply ray-tracing with diffraction methods; however, accuracy depends critically on both geometric discretization and diffraction modeling. This work investigates ray-tracing-based modeling of near-field scattering from curved bodies, both in the backscattering and in the forward (shadow) region; in the ray-tracing tool, diffraction is modeled according to the Uniform Theory of Diffraction (UTD), extended with vertex diffraction and double-bounce interactions, including a heuristic combination of edge and vertex diffraction. A discretization strategy linking facet size to local curvature and wavelength is proposed to balance geometric fidelity, diffraction modeling, and efficiency. Validation is initially performed against analytical solutions and full-wave simulations for canonical geometries (sphere and circular cylinder). Furthermore, the practical applicability of the approach is demonstrated for a realistic vehicle by comparison with bistatic measurements in the backscattering region and full-wave simulation in the shadow region. The results demonstrate that no universal discretization strategy exists: fine meshes are beneficial for accurate backscattering prediction, while coarser discretizations can provide more efficient and accurate shadow region prediction. The proposed extended diffraction framework provides a computationally efficient framework for vehicular propagation and integrated sensing and communication (ISAC) channel modeling.

Submission history

From: Ainur Ziganshin [view email]
[v1] Tue, 7 Apr 2026 15:18:05 UTC (4,247 KB)
[v2] Thu, 2 Jul 2026 13:54:24 UTC (9,853 KB)