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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.07285 |
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| _version_ | 1866911048442839040 |
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| author | Zirak, Kavian Imani, Mohammadreza F. |
| author_facet | Zirak, Kavian Imani, Mohammadreza F. |
| contents | This paper introduces an innovative imaging method using reconfigurable intelligent surfaces (RISs) by combining radar coincidence imaging (RCI) and computational imaging techniques. In the proposed framework, RISs simultaneously redirect beams toward a desired region of interest (ROI). The interference of these beams forms spatially diverse speckle patterns that carry information about the entire ROI. As a result, this method can take advantage of the benefits of both random patterns and spotlight imaging. Since the speckle pattern is formed by directive beams (instead of random patterns typically used in computational imaging), this approach results in a higher signal-to-noise ratio (SNR) and reduced clutter. In contrast to raster scanning, which requires the number of measurements to be at least equal to the number of unknowns, our proposed approach follows a computational imaging framework and can obtain high-quality images even when only a few measurements are taken. Using numerical simulation, we demonstrate this method's capabilities and contrast it against other conventional techniques. The proposed imaging approach can be applied to security screening, wireless user tracking, and activity recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07285 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A RIS-Enabled Computational Radar Coincidence Imaging Zirak, Kavian Imani, Mohammadreza F. Signal Processing This paper introduces an innovative imaging method using reconfigurable intelligent surfaces (RISs) by combining radar coincidence imaging (RCI) and computational imaging techniques. In the proposed framework, RISs simultaneously redirect beams toward a desired region of interest (ROI). The interference of these beams forms spatially diverse speckle patterns that carry information about the entire ROI. As a result, this method can take advantage of the benefits of both random patterns and spotlight imaging. Since the speckle pattern is formed by directive beams (instead of random patterns typically used in computational imaging), this approach results in a higher signal-to-noise ratio (SNR) and reduced clutter. In contrast to raster scanning, which requires the number of measurements to be at least equal to the number of unknowns, our proposed approach follows a computational imaging framework and can obtain high-quality images even when only a few measurements are taken. Using numerical simulation, we demonstrate this method's capabilities and contrast it against other conventional techniques. The proposed imaging approach can be applied to security screening, wireless user tracking, and activity recognition. |
| title | A RIS-Enabled Computational Radar Coincidence Imaging |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.07285 |