Reinforced Inverse Scattering
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866914267257634816 |
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| author | Jiang, Hanyang Khoo, Yuehaw Yang, Haizhao |
| author_facet | Jiang, Hanyang Khoo, Yuehaw Yang, Haizhao |
| contents | Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties. This paper introduces reinforcement learning to develop precision imaging that decides sensor positions and wave frequencies adaptive to different scatterers in an intelligent way, thus obtaining a significant improvement in reconstruction quality with limited imaging resources. Extensive numerical results will be provided to demonstrate the superiority of the proposed method over existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_04186 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Reinforced Inverse Scattering Jiang, Hanyang Khoo, Yuehaw Yang, Haizhao Machine Learning Signal Processing 68Txx, 49MXX, 65N21 Inverse wave scattering aims at determining the properties of an object using data on how the object scatters incoming waves. In order to collect information, sensors are put in different locations to send and receive waves from each other. The choice of sensor positions and incident wave frequencies determines the reconstruction quality of scatterer properties. This paper introduces reinforcement learning to develop precision imaging that decides sensor positions and wave frequencies adaptive to different scatterers in an intelligent way, thus obtaining a significant improvement in reconstruction quality with limited imaging resources. Extensive numerical results will be provided to demonstrate the superiority of the proposed method over existing methods. |
| title | Reinforced Inverse Scattering |
| topic | Machine Learning Signal Processing 68Txx, 49MXX, 65N21 |
| url | https://arxiv.org/abs/2206.04186 |