应用改进的主要特征基函数快速计算目标宽角度RCS

王仲根; 唐晓菀; 汪强

doi:10.11999/JEIT170499

应用改进的主要特征基函数快速计算目标宽角度RCS

doi: 10.11999/JEIT170499

基金项目:

国家自然科学基金(61401003)，安徽省教育厅自然科学基金(KJ2016A669)

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- 被引次数: 0
出版历程
- 收稿日期: 2017-05-24
- 修回日期: 2017-10-30
- 刊出日期: 2018-03-19

Fast Calculation of Wide-angel RCS of Objects Using Improved Primary Characteristic Basis Functions

Funds:

The National Natural Science Foundation of China (61401003), The Natural Science Foundation of Anhui Provincial Education Department (KJ2016A669)

摘要

摘要: 特征基函数法是分析目标宽角度电磁散射特性的有效方法之一，但在构造特征基函数时，设置的入射波激励包含大量的冗余信息，大大降低了特征基函数的构造效率；另外在分析复杂目标时，在增加激励数目的情况下，仅应用主要特征基函数并不能显著提高计算精度。针对这些问题，该文对特征基函数构造方法进行改进，首先采用奇异值分解技术对激励矩阵进行压缩去除冗余信息，减少求解矩阵方程的次数；其次充分考虑子域之间的互耦作用，将主要特征基函数与次要特征基函数融合，得到改进的主要特征基函数。数值计算结果表明：与传统方法相比，该方法具有更高的计算效率和计算精度。
- 宽角度RCS /
- 特征基函数法 /
- 特征基函数 /
- 奇异值分解
Abstract: Characteristic basis function method is one of the effective methods to analyze wide-angle electromagnetic scattering characteristics of objects. However, the incident wave excitations used to construct the Characteristic Basis Functions (CBFs) contain large amount of redundant information, which greatly reduces the construction efficiency of the CBFs. Moreover, when the complex target is analyzed, the calculation accuracy can not be significantly improved only using the Primary CBFs (PCBFs) when the number of excitations is increased. To solve these problems, an improved CBFs construction method is presented in this paper. Firstly, the Singular Value Decomposition (SVD) technique is used to effectively compress the excitation matrix to remove the redundant information, which in turn reduces the number of solving the matrix equation. Then, the mutual interaction among subdomains is fully considered, the Improved PCBFs (IPCBFs) are obtained by merging the PCBFs and the Secondary CBFs (SCBFs). The numerical results show that the proposed method has higher computational efficiency and computational accuracy than the traditional method.
- Wide-angle RCS /
- Characteristic Basis Function Method (CBFM) /
- Characteristic Basis Functions (CBFs) /
- Singular Value Decomposition (SVD)

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