應(yīng)用改進(jìn)的主要特征基函數(shù)快速計(jì)算目標(biāo)寬角度RCS

王仲根; 唐曉菀; 汪強(qiáng)

doi:10.11999/JEIT170499

應(yīng)用改進(jìn)的主要特征基函數(shù)快速計(jì)算目標(biāo)寬角度RCS

doi: 10.11999/JEIT170499

基金項(xiàng)目:

國(guó)家自然科學(xué)基金(61401003)，安徽省教育廳自然科學(xué)基金(KJ2016A669)

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出版歷程
- 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: 特征基函數(shù)法是分析目標(biāo)寬角度電磁散射特性的有效方法之一，但在構(gòu)造特征基函數(shù)時(shí)，設(shè)置的入射波激勵(lì)包含大量的冗余信息，大大降低了特征基函數(shù)的構(gòu)造效率；另外在分析復(fù)雜目標(biāo)時(shí)，在增加激勵(lì)數(shù)目的情況下，僅應(yīng)用主要特征基函數(shù)并不能顯著提高計(jì)算精度。針對(duì)這些問(wèn)題，該文對(duì)特征基函數(shù)構(gòu)造方法進(jìn)行改進(jìn)，首先采用奇異值分解技術(shù)對(duì)激勵(lì)矩陣進(jìn)行壓縮去除冗余信息，減少求解矩陣方程的次數(shù)；其次充分考慮子域之間的互耦作用，將主要特征基函數(shù)與次要特征基函數(shù)融合，得到改進(jìn)的主要特征基函數(shù)。數(shù)值計(jì)算結(jié)果表明：與傳統(tǒng)方法相比，該方法具有更高的計(jì)算效率和計(jì)算精度。
- 寬角度RCS /
- 特征基函數(shù)法 /
- 特征基函數(shù) /
- 奇異值分解
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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