Enhanced Compressive Sensing Method of Moments via Physics-Aware Characteristic Modes and LSQR Solver.

Saved in:

Bibliographic Details
Title:	Enhanced Compressive Sensing Method of Moments via Physics-Aware Characteristic Modes and LSQR Solver.
Authors:	Liu, Yang¹ 2023200714@aust.edu.cn, Wang, Zhonggen¹ zgwang@ahu.edu.cn, Nie, Wenyan² wynie5240@163.com, Sun, Longhui¹ 15556361695@163.com
Source:	Applied Computational Electromagnetics Society Journal. Mar2026, Vol. 41 Issue 3, p288-296. 9p.
Subjects:	Compressed sensing, Iterative methods (Mathematics), Electromagnetic wave scattering, Optimization algorithms
Abstract:	To improve the computational efficiency and stability of the compressive sensing-method of moments (CS-MoM) based on characteristic mode basis functions (CMBFs) for electromagnetic scattering problems, this paper introduces an enhanced construction strategy for CMBFs. The proposed method adopts a dual strategy framework that synergistically integrates physical insight with mathematical screening, replacing the conventional approach based solely on mathematical selection. This integration significantly enhances the physical interpretability and sparsity of the resulting basis functions. In addition, the least squares QR (LSQR) iterative algorithm, which solves the problem by utilizing QR decomposition, is employed instead of the traditional LS method for the CS reconstruction problem. This replacement alleviates the detrimental effects of ill-conditioned matrices on solution stability, thereby improving the robustness and accuracy of the algorithm. Numerical results confirm that the proposed method substantially reduces computational complexity while enhancing numerical stability. [ABSTRACT FROM AUTHOR]
	Copyright of Applied Computational Electromagnetics Society Journal is the property of River Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	To improve the computational efficiency and stability of the compressive sensing-method of moments (CS-MoM) based on characteristic mode basis functions (CMBFs) for electromagnetic scattering problems, this paper introduces an enhanced construction strategy for CMBFs. The proposed method adopts a dual strategy framework that synergistically integrates physical insight with mathematical screening, replacing the conventional approach based solely on mathematical selection. This integration significantly enhances the physical interpretability and sparsity of the resulting basis functions. In addition, the least squares QR (LSQR) iterative algorithm, which solves the problem by utilizing QR decomposition, is employed instead of the traditional LS method for the CS reconstruction problem. This replacement alleviates the detrimental effects of ill-conditioned matrices on solution stability, thereby improving the robustness and accuracy of the algorithm. Numerical results confirm that the proposed method substantially reduces computational complexity while enhancing numerical stability. [ABSTRACT FROM AUTHOR]
ISSN:	10544887
DOI:	10.13052/2026.ACES.J.410310