Accelerated Characteristic Mode Calculation for PEC Objects Using ACA-QR-SVD Algorithm

Abstract

Characteristic mode (CM) analysis serves as a powerful tool for evaluating the radiation and scattering characteristics of objects. CM formulations within the method of moments (MoM) framework are widely favored due to their ability to offer clear physical insights, handle complex shapes, and facilitate straightforward implementation. However, MoM-based CM formulations become inefficient when applied to electrically large objects due to the dense matrices involved. This article introduces a novel approach using a fast low-rank decomposition-based implicitly restarted Arnoldi method (IRAM) to accelerate CM computations. The adaptive cross approximation (ACA) and QR-SVD algorithms are employed to efficiently compute the low-rank decomposition of matrices. The ACA-QR-SVD algorithm offers advantages in matrix filling, LU factorization, and matrix–vector multiplication processes, thereby enhancing efficiency. Numerical simulations on two representative objects demonstrate that the proposed algorithm notably improves computational speed and reduces memory requirements while maintaining high computational accuracy.