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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2208.10944 |
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| _version_ | 1866929198591901696 |
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| author | Ye, Xiuzhu Zardi, Francesco Salucci, Marco Massa, Andrea |
| author_facet | Ye, Xiuzhu Zardi, Francesco Salucci, Marco Massa, Andrea |
| contents | Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_10944 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors Ye, Xiuzhu Zardi, Francesco Salucci, Marco Massa, Andrea Signal Processing Perfect Electric Conductors (PECs) are imaged integrating the subspace-based optimizationmethod (SOM) within the iterative multi-scaling scheme (IMSA). Without a-priori information on the number or/and the locations of the scatterers and modelling their EM scattering interactions with a (known) probing source in terms of surface electric field integral equations, a segment-based representation of PECs is retrieved from the scattered field samples. The proposed IMSA-SOM inversion method is validated against both synthetic and experimental data by assessing the reconstruction accuracy, the robustness to the noise, and the computational efficiency with some comparisons, as well. |
| title | Multi-Resolution Subspace-Based Optimization Method for the Retrieval of 2D Perfect Electric Conductors |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2208.10944 |