Fast Solution Of RCS Of Conducting Targets Based On Fast Dipole Method

The analysis of electromagnetic scattering characteristics of target is one of the most important research directions in the field of computational electromagnetics.Geological surveying,radar detection and object identification have been widely used in practical applications.The accurate and efficient analysis of electromagnetic scattering characteristics of some electrically large or complex structures have been one of the hot focus in the field of computational electromagnetics.The method of moments(MoM)has the characteristics of high precision and high efficiency.The MoM is one of the numerical methods to analyze the electromagnetic scattering characteristics.But the traditional MOM will need too long computation time and is low efficiency to analyze electrically large targets.In order to solve this problem,two methods are proposed to calculate the radar cross section(RCS)of conductor targets.The first method is the fast dipole method(FDM)combined with recompressed adaptive cross approximation(RACA)algorithm used to solve the electromagnetic scattering from perfect conducting targets.Fast dipole method is helpful for the fast calculation of matrix product of far field group.In order to speed up the calculation of mutual impedance elements in the near-field groups,the RACA algorithm is used to further compress the impedance matrix.The computational time and memory consumption of the proposed method are reduced effectively compared with the traditional FDM.The second method is effective analysis method of target electromagnetic scattering problems,based on characteristic basis function method.FDM combined with CBFM-II,which is used to accelerate the matrix vector multiplication procedure in the generation of Secondary level Characteristic Basis Function(SCBF)and construction of reduced matrix in the field of far-field groups.The ACA algorithm is also used to further accelerate the filling of the impedance matrix of the near field,which can improve the computing efficiency and save the memory.