Fast Computation of EM Scattering From Moving PEC Target Based on MT-ACA-MoM Algorithm

• Published in: IEEE antennas and wireless propagation letters Vol. 23; no. 12; pp. 4628 - 4632
• Main Authors: Chai, Shui-Rong, Meng, Ling-Hui, Zou, Yu-Feng, Dai, Pu-Kun
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptive cross approximation (ACA); Algorithms; Computing time; Electric conductors; Excitation; Filling; Green's functions; Impedance; Impedance matrix; Mathematical models; Matrix decomposition; method of moment (MoM); Method of moments; Movement; moving target; Rotation; Scattering; Vectors
• ISSN: 1536-1225; 1548-5757
• DOI: 10.1109/LAWP.2024.3462446

To solve the electromagnetic (EM) scattering problems involving a single, complexly moving perfectly electric conductor (PEC) with the traditional adaptive cross approximation-method of moment (ACA-MoM), the excitation vector and the impedance matrix need to be established repeatedly. Moreover, the matrix equation must be iteratively solved at each point in the movement trajectory. This leads to an increase in computational time, especially for targets exhibiting intricate movements. To address these challenges, a new algorithm named multiple time ACA-MoM (MT-ACA-MoM) is proposed and validated in this letter. This algorithm exploits the translation rotation invariance of Green's function. Consequently, regardless of the target's rotation and translation, the impedance matrix remains unchanged. Therefore, only one impedance matrix needs to be established and stored, which significantly reduces filling time. For the multiple-time problems, the excitation matrix exhibits high-rank deficiency. Therefore, in MT-ACA-MoM, ACA is utilized to compress both the impedance matrix and the excitation matrix. Numerical results demonstrate a reduction in the number of matrix equations from 2000 to approximately 100 to 200, leading to a further decrease in solving time of up to 94% and a total speedup of over 700 times.