Machine–learning-enabled metasurface for direction of arrival estimation
| Autor: | Huang Min, Zheng Bin, Cai Tong, Li Xiaofeng, Liu Jian, Qian Chao, Chen Hongsheng |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Nanophotonics, Vol 11, Iss 9, Pp 2001-2010 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2192-8614 2021-0663 |
| DOI: | 10.1515/nanoph-2021-0663 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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