Machine–learning-enabled metasurface for direction of arrival estimation
Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal a...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-01-01
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| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2021-0663 |
| _version_ | 1828059010285174784 |
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| author | Huang Min Zheng Bin Cai Tong Li Xiaofeng Liu Jian Qian Chao Chen Hongsheng |
| author_facet | Huang Min Zheng Bin Cai Tong Li Xiaofeng Liu Jian Qian Chao Chen Hongsheng |
| author_sort | Huang Min |
| collection | DOAJ |
| description | Metasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for in-situ detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than 0.5°
$0.5{}^{\circ}$
. The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization. |
| first_indexed | 2024-04-10T21:35:29Z |
| format | Article |
| id | doaj.art-f79fbbe258ed46fea71bcb0773d42933 |
| institution | Directory Open Access Journal |
| issn | 2192-8614 |
| language | English |
| last_indexed | 2024-04-10T21:35:29Z |
| publishDate | 2022-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj.art-f79fbbe258ed46fea71bcb0773d429332023-01-19T12:46:59ZengDe GruyterNanophotonics2192-86142022-01-011192001201010.1515/nanoph-2021-0663Machine–learning-enabled metasurface for direction of arrival estimationHuang Min0Zheng Bin1Cai Tong2Li Xiaofeng3Liu Jian4Qian Chao5Chen Hongsheng6Interdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaInterdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaInterdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaAir and Missile Defense College, Air Force Engineering University, Xi’an710051, ChinaInterdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaInterdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaInterdisciplinary Center for Quantum Information, State Key Laboratory of Modern Optical Instrumentation, ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou310027, ChinaMetasurfaces, interacted with artificial intelligence, have now been motivating many contemporary research studies to revisit established fields, e.g., direction of arrival (DOA) estimation. Conventional DOA estimation techniques typically necessitate bulky-sized beam-scanning equipment for signal acquisition or complicated reconstruction algorithms for data postprocessing, making them ineffective for in-situ detection. In this article, we propose a machine-learning-enabled metasurface for DOA estimation. For certain incident signals, a tunable metasurface is controlled in sequence, generating a series of field intensities at the single receiving probe. The perceived data are subsequently processed by a pretrained random forest model to access the incident angle. As an illustrative example, we experimentally demonstrate a high-accuracy intelligent DOA estimation approach for a wide range of incident angles and achieve more than 95% accuracy with an error of less than 0.5° $0.5{}^{\circ}$ . The reported strategy opens a feasible route for intelligent DOA detection in full space and wide band. Moreover, it will provide breakthrough inspiration for traditional applications incorporating time-saving and equipment-simplified majorization.https://doi.org/10.1515/nanoph-2021-0663direction of arrival estimationmetasurfacerandom forest |
| spellingShingle | Huang Min Zheng Bin Cai Tong Li Xiaofeng Liu Jian Qian Chao Chen Hongsheng Machine–learning-enabled metasurface for direction of arrival estimation Nanophotonics direction of arrival estimation metasurface random forest |
| title | Machine–learning-enabled metasurface for direction of arrival estimation |
| title_full | Machine–learning-enabled metasurface for direction of arrival estimation |
| title_fullStr | Machine–learning-enabled metasurface for direction of arrival estimation |
| title_full_unstemmed | Machine–learning-enabled metasurface for direction of arrival estimation |
| title_short | Machine–learning-enabled metasurface for direction of arrival estimation |
| title_sort | machine learning enabled metasurface for direction of arrival estimation |
| topic | direction of arrival estimation metasurface random forest |
| url | https://doi.org/10.1515/nanoph-2021-0663 |
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