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Compressive Sensing Enhanced Multilevel Characteristic Basis Function Method for Analyzing Electrically Large Scattering Problems
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AI
DOI：10.1109/LAWP.2023.3330820.png)
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摘要
En 中文
A novel method using the compressive sensing (CS) technique to accelerate the multilevel characteristic basis function method (MLCBFM) is proposed in this letter for the solution of electrically large scattering problems. According to the MLCBFM, the solution process involves the full filling of the impedance matrix as well as the construction and solution of the reduced matrix equation on each level, which is time-consuming. Unlike this, we use the CS technique on each level, except for the bottom level, to recover the coefficients of CBFs and total current, through which both memory consumption and computation time would enjoy a significant reduction. Furthermore, we optimize the generalized orthogonal matching pursuit recovery algorithm to the least squares method, which achieves more stable recovery accuracy and higher efficiency. The results of numerical simulations indicate that the proposed method is much more efficient and less memory-consuming compared to the MLCBFM while obtaining a higher level of accuracy.
Keyword:
Compressive sensing (CS)
multilevel characteristic basis function method (MLCBFM)
multilevel division
radar cross section (RCS)
期刊
IF:
4.8
论文数:
1.0W
被引数:
2.8W
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