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一种用于分析复合导电介质物体在多角度入射下电磁散射特性的有效解决方案。

An effective solution for analyzing the electromagnetic scattering characteristics of composite conducting dielectric objects under multiple angle incidence.

作者信息

Kong Meng, Cao Xin Yuan, Qi Qi, Wu Xian Liang

机构信息

Anhui Province Key Laboratory of Simulation Calculation and Design for Electronic Information System, Hefei Normal University, Hefei, 230601, China.

出版信息

Sci Rep. 2025 Jul 23;15(1):26844. doi: 10.1038/s41598-025-11786-1.

Abstract

The accurate modeling of electromagnetic scattering from composite conducting-dielectric objects under multi-angle incidence is crucial in the field of electromagnetic engineering, however, due to the huge computational resources required, there is a disadvantage of slow analysis speed on personal computer platforms. Based on the compressive sensing theory, this article constructs a new excitation source solution model using the Volume-Surface Integral Equation platform. The special CS technique is separately applied to the conductive surface and dielectric region of the composite object, respectively, in order to improve the efficiency of analyzing the wide-angle electromagnetic scattering characteristics of the composite object. Compared with the traditional Volume-Surface Integral Equation method, the proposed method can significantly reduce the number of repeated calculations of matrix equations while maintaining the same level of accuracy and memory consumption, so as to accelerate the overall calculation of the wide-angle electromagnetic scattering. By the complexity analysis of the proposed algorithm and the numerical simulation of various composite objects with different materials and structures, the efficiency of the proposed algorithm is verified.

摘要

在多角度入射情况下,对复合导电-电介质物体的电磁散射进行精确建模在电磁工程领域至关重要,然而,由于所需的计算资源巨大,在个人计算机平台上存在分析速度慢的缺点。基于压缩感知理论,本文利用体积-表面积分方程平台构建了一种新的激励源求解模型。将特殊的压缩感知技术分别应用于复合物体的导电表面和电介质区域,以提高分析复合物体广角电磁散射特性的效率。与传统的体积-表面积分方程方法相比,该方法在保持相同精度和内存消耗水平的同时,能显著减少矩阵方程的重复计算次数,从而加速广角电磁散射的整体计算。通过对所提算法的复杂度分析以及对不同材料和结构的各种复合物体的数值模拟,验证了所提算法的效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7063/12287359/4d7615e84cc2/41598_2025_11786_Fig1_HTML.jpg

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