Identification of Radio Frequency Interference Using Multi-scale TransUNet
文献类型：期刊论文
| 作者 | Zhang, Xuan2; Liang, Bo2; Hao LF(郝龙飞)1 ; Feng, Song2; Wei, Shoulin2; Dai, Wei2; Dao, Yihang2
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| 刊名 | PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC
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| 出版日期 | 2024-06-01 |
| 卷号 | 136期号:6 |
| ISSN号 | 0004-6280 |
| DOI | 10.1088/1538-3873/ad54ef |
| 产权排序 | 第2完成单位 |
| 文献子类 | Article |
| 英文摘要 | Radio observation is a method for conducting astronomical observations using radio waves. A common challenge in radio observations is Radio Frequency Interference (RFI), which refers to the unintentional or intentional interference of radio signals from other wireless sources within the radio frequency band. Such interference contaminates the astronomical signals received by radio telescopes, significantly affecting time-frequency domain astronomical observations and research. Consequently, identifying RFI is crucial. In this paper, we employ a deep learning approach to detect RFI present in observation data and propose an improved network structure based on TransUNet. This network leverages the principles of a multi-scale convolutional attention mechanism. It introduces an auxiliary branch to extract high-dimensional image information and an enhanced coordinate attention mechanism for feature map extraction, enabling more comprehensive and accurate identification of RFI in time-frequency images. We introduce a novel architecture named the Multi-Scale TransUNet Network, abbreviated as MS-TransUNet. We utilized observation data from the 40 m radio telescope at the Yunnan Observatory as a data set for training, validating, and testing the network. Compared with previous deep learning networks (U-Net, RFI-Net, R-Net, DSC, EMSCA-UNet), the recall rate and f2 score have been significantly improved. Specifically, the recall rate is improved by at least 2.99%, and the f2 score is improved by at least 2.46%. Experiments demonstrate that this network is exceptional in identifying RFI more comprehensively while ensuring high precision. |
| 学科主题 | 天文学 ; 射电天文学 |
| URL标识 | 查看原文 |
| 出版地 | TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND |
| WOS关键词 | MITIGATION |
| 资助项目 | National Key Research and Development Program of China[2020SKA0110300]; National Key Research and Development Program of China[2020SKA0120100]; National Natural Science Foundation of China[12063003]; National Natural Science Foundation of China[12073076]; Yunnan Ten Thousand Talents Plan Young & Elite Talents Project; Yunnan Key Laboratory of Computer Technologies Application |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| WOS记录号 | WOS:001259073800001 |
| 出版者 | IOP Publishing Ltd |
| 资助机构 | National Key Research and Development Program of China[2020SKA0110300, 2020SKA0120100] ; National Natural Science Foundation of China[12063003, 12073076] ; Yunnan Ten Thousand Talents Plan Young & Elite Talents Project ; Yunnan Key Laboratory of Computer Technologies Application |
| 版本 | 出版稿 |
| 源URL | [http://ir.ynao.ac.cn/handle/114a53/27435]  |
| 专题 | 云南天文台_射电天文研究组 |
| 作者单位 | 1.Yunnan Observatories, Chinese Academy of Science, Kunming, 650000, People's Republic of China 2.Faculty of Information Engineering and Automation, Kunming University of Science and Technology and Yunnan Key Laboratory of Computer Technologies Application, Kunming, 650500, People's Republic of China; |
| 推荐引用方式 GB/T 7714 | Zhang, Xuan,Liang, Bo,Hao LF,et al. Identification of Radio Frequency Interference Using Multi-scale TransUNet[J]. PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,2024,136(6). |
| APA | Zhang, Xuan.,Liang, Bo.,郝龙飞.,Feng, Song.,Wei, Shoulin.,...&Dao, Yihang.(2024).Identification of Radio Frequency Interference Using Multi-scale TransUNet.PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC,136(6). |
| MLA | Zhang, Xuan,et al."Identification of Radio Frequency Interference Using Multi-scale TransUNet".PUBLICATIONS OF THE ASTRONOMICAL SOCIETY OF THE PACIFIC 136.6(2024). |
入库方式： OAI收割
来源：云南天文台
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