Unveiling novel insights into Kirchhoff migration for effective object detection using experimental Fresnel dataset
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910684267151360 |
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| author | Park, Won-Kwang |
| author_facet | Park, Won-Kwang |
| contents | This study investigates the applicability of Kirchhoff migration (KM) for a fast identification of unknown objects in a real-world limited-aperture inverse scattering problem. To demonstrate the theoretical basis for the applicability including unique determination of objects, the imaging function of the KM was formulated using a uniformly convergent infinite series of Bessel functions of integer order of the first kind based on the integral equation formula for the scattered field. Numerical simulations performed using the experimental Fresnel dataset are exhibited to achieve the theoretical results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_18322 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unveiling novel insights into Kirchhoff migration for effective object detection using experimental Fresnel dataset Park, Won-Kwang Numerical Analysis 78A46 This study investigates the applicability of Kirchhoff migration (KM) for a fast identification of unknown objects in a real-world limited-aperture inverse scattering problem. To demonstrate the theoretical basis for the applicability including unique determination of objects, the imaging function of the KM was formulated using a uniformly convergent infinite series of Bessel functions of integer order of the first kind based on the integral equation formula for the scattered field. Numerical simulations performed using the experimental Fresnel dataset are exhibited to achieve the theoretical results. |
| title | Unveiling novel insights into Kirchhoff migration for effective object detection using experimental Fresnel dataset |
| topic | Numerical Analysis 78A46 |
| url | https://arxiv.org/abs/2402.18322 |