Abstract
The multilevel fast multipole algorithm (MLFMA) using K-means clustering to accelerate electromagnetic scattering analysis for large complex targets is presented. By replacing the regular cube grouping with the K-means clustering, the addition theorem is more accurately approximated. The convergence rate of an iterative solver is thus improved significantly. However, irregular centroid locations as a result of the K-means clustering increase the amount of explicit transfer function calculations, compared with the regular cubes. In the MLFMA, a multilevel hierarchical structure is applied to the finite multipole method (FMM) to reduce transfer function calculations. Therefore, the MLFMA is suitable for applying K-means clustering. Simulation results with both canonical and realistic targets show an improvement in the computation time of the proposed algorithm.
Highlights
Radar cross-section (RCS) characteristics represent electromagnetic scattering phenomena of targets [1]
Algorithm is essential to calculate the electromagnetic scattering of electrically large targets, e.g., stealth aircraft
The simulation resultsresults with thewith realistic presented faster convergence large complex targets
Summary
Radar cross-section (RCS) characteristics represent electromagnetic scattering phenomena of targets [1]. Radar systems recognize an unknown target based on the RCS or additional data refined from the RCS [2,3]. With the rapid progress of computational electromagnetics (CEM), diverse electromagnetic scattering problems, which could not be solved analytically previously, are solved numerically [4]. The computational cost of the numerical analysis increases significantly due to a large number of unknowns regarding electrically large targets. An acceleration of the CEM algorithm is essential to calculate the electromagnetic scattering of electrically large targets, e.g., stealth aircraft. In the conventional numerical analysis for electromagnetic scattering problems, the method of moment (MoM) has been widely used to discretize the surface integral equations into a linear problem [5,6,7]
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