Scattering analysis of mixed metallic/uniaxial objects using surface integral equations accelerated by adaptive cross approximation algorithm

Accurate and cost-effective simulation of electromagnetic wave interactions with composite bodies that consist of conductors and complex materials with a variety of constitutive relationships has been of considerable interest owing to the wide application of materials in a variety of radar targets. Among these complex materials, anisotropic materials [1-3] have been one of the most promising topics in electromagnetic research. A variety of approaches have been developed to analyze these problems, such as the method of moments (MoM), the finite element method (FEM) and the finite difference time domain (FDTD) method. When the dielectric objects are homogeneous or piecewise homogeneous, MoM based on surface integral equations (SIE) is preferred because it limits the discretization of unknown quantities to the surfaces of the objects. Despite this, the traditional MoM incurs a very high computational cost and memory requirement. Recently, the adaptive across approximation (ACA) algorithm [4] has been proposed to reduce this cost. This reduction is achieved through linear algebra manipulation of the MoM impedance matrix. To date, this algorithm has been applied to low-frequency and full-wave EM problems including conducting objects, in which computational complexity is O(N log N) as compared with O(N²) of the traditional MoM. In this paper, we adopt the ACA algorithm to calculate electromagnetic scattering from three-dimensional (3D) bodies consisting of both conducting and uniaxial anisotropic parts. The Poggio-Miller-Change-Harrington-Wu-Tsai (PMCHWT) equations and the electric field integral equation (EFIE) are formulated for uniaxial anisotropic media and conducting objects, respectively. The ACA algorithm is utilized to accelerate the matrix-vector multiplication in the iterative solution process. Numerical results demonstrate the good performance of the proposed algorithm.