中国科学技术大学学报 ›› 2017, Vol. 47 ›› Issue (10): 843-847.DOI: 10.3969/j.issn.0253-2778.2017.10.006

• 论著 • 上一篇    下一篇

一种基于SVD-CBFM和RACA的单站RCS快速求解方法

朱金玉,孙玉发,方红雨,丁亚辉,杨明   

  1. 安徽大学计算智能与信号处理教育部重点实验室,安徽合肥 230601
  • 收稿日期:2017-01-09 修回日期:2017-05-27 出版日期:2017-10-31 发布日期:2017-10-31
  • 通讯作者: 孙玉发
  • 作者简介:朱金玉,女,1992年生,硕士生. 研究方向:电磁散射与计算. E-mail:874421334@qq.com
  • 基金资助:
    国家自然科学基金(61172020)资助.

A fast solution to monostatic RCS based on SVD-CBFM and RACA

ZHU Jinyu, SUN Yufa, FANG Hongyu, DING Yahui, YANG Ming   

  1. Key Lab of Intelligent Computing & Signal Processing, Ministry of Education, Anhui University, Hefei 230601, China)
  • Received:2017-01-09 Revised:2017-05-27 Online:2017-10-31 Published:2017-10-31

摘要: 在奇异值分解特征基函数法(SVD-CBFM)的基础上提出一种快速求解目标单站RCS的有效数值方法.为了降低入射激励的数目,该方法考虑了子域间的相互耦合作用,计算出各子域的次要特征基函数(SCBF).采用再压缩自适应交叉近似 (RACA) 算法对各子域的特征基函数(CBFs)进行压缩,加速CBFs的生成.同时运用RACA算法填充远场区阻抗矩阵,从而进一步提高SCBF和缩减矩阵构造过程中的矩阵矢量相乘速度.数值算例验证了该方法的精准性和有效性.

关键词: 矩量法, 自适应交叉近似, 奇异值分解, 特征基函数法

Abstract: An efficient method was proposed to solve monostatic RCS based on singular value decomposition-characteristic basis function method (SVD-CBFM). To reduce the numbers of incident wave excitations, the method considers the coupling effect among the sub-blocks, and calculates the secondary characteristic basis function (SCBF) of each sub-block. The recompressed adaptive cross approximation (RACA) algorithm was applied to recompress the characteristic basis functions (CBFs), which can accelerate the generation of CBFs. In order to further improve the speed of the matrix vector multiplication in the construction process of the SCBF and reduced matrix, the RACA algorithm was also applied to fill the impedance matrix of the far field. The numerical examples demonstrate the accuracy and efficiency of the proposed method.

Key words: method of moment, adaptive cross approximation, singular value decomposition, characteristic basis function method

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