Rapid calculation of bistatic scattering problems based on bayesian compressive sensing.
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| Title: | Rapid calculation of bistatic scattering problems based on bayesian compressive sensing. |
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| Authors: | Wang, Zhonggen1 (AUTHOR), Sun, Longhui1 (AUTHOR) 15556361695@163.com, Nie, Wenyan2 (AUTHOR), Sun, Yufa3 (AUTHOR), Dong, Dai1 (AUTHOR), Liu, Yang1 (AUTHOR) |
| Source: | Electromagnetics. 2025, Vol. 45 Issue 4, p320-333. 14p. |
| Subjects: | Orthogonal matching pursuit, Characteristic functions, Numerical calculations, Algorithms |
| Abstract: | In this paper, in order to accelerate the solution of the three-dimensional target scattering problems, bayesian compressive sensing (BCS) combined with high-order characteristic basis functions (HCBFs) is proposed. Unlike the traditional compressive sensing (CS) combined with CBFs method, the main improvements of our work are twofold: First, by utilizing HCBFs instead of traditional CBFs, not only the construction of the sparse basis is accelerated, but also the computational accuracy is improved. Second, comparing to CS using the orthogonal matching pursuit recovery algorithm, BCS requires fewer iterations and takes less time in recovering sparse signals. Numerical calculations show that the new method not only accelerates the solution time but also improves the computational accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In this paper, in order to accelerate the solution of the three-dimensional target scattering problems, bayesian compressive sensing (BCS) combined with high-order characteristic basis functions (HCBFs) is proposed. Unlike the traditional compressive sensing (CS) combined with CBFs method, the main improvements of our work are twofold: First, by utilizing HCBFs instead of traditional CBFs, not only the construction of the sparse basis is accelerated, but also the computational accuracy is improved. Second, comparing to CS using the orthogonal matching pursuit recovery algorithm, BCS requires fewer iterations and takes less time in recovering sparse signals. Numerical calculations show that the new method not only accelerates the solution time but also improves the computational accuracy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 02726343 |
| DOI: | 10.1080/02726343.2025.2462925 |
