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.
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.
基金
国家自然科学基金(61172020)资助
关键词
矩量法
自适应交叉近似
奇异值分解
特征基函数法
method of moment
adaptive cross approximation
singular value decomposition
characteristic basis function method
