{"title":"Radio frequency interference identification using dual cross-attention and multi-scale feature fusing","authors":"","doi":"10.1016/j.ascom.2024.100881","DOIUrl":null,"url":null,"abstract":"<div><div>Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of <span><math><mrow><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span>, <span><math><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span>, and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn><mo>−</mo><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></math></span>, respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in <span><math><mrow><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span> and <span><math><msub><mrow><mi>F</mi></mrow><mrow><mn>1</mn><mo>−</mo><mi>s</mi><mi>c</mi><mi>o</mi><mi>r</mi><mi>e</mi></mrow></msub></math></span>, effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.</div></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Astronomy and Computing","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213133724000969","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Radio astronomy plays a very important role in promoting scientific progress and unraveling the mysteries of the universe. However, radio telescopes are inevitably affected by radio frequency interference (RFI) when receiving radio signals, which leads to a reduction in data quality and has a serious impact on the formation of correct scientific conclusions. Therefore, it is essential to identify the RFI present in the observational data. In order to effectively identify RFI, improve the existing RFI identification methods that suffer from missed detections, and enhance the performance of RFI identification, this paper proposes a novel method that combines a dual cross-attention mechanism with multi-scale feature fusion. Experimental studies were conducted using the observational data from the 40-meter radio telescope at the Yunnan Astronomical Observatory of the Chinese Academy of Sciences. The proposed method achieved scores of 92.49%, 83.90%, and 87.99% in terms of , , and , respectively. It outperformed existing methods (U-Net, RFI-Net, R-Net6, RFI-GAN, EMSCA-UNet) in and , effectively reducing the occurrence of missed detections and improving the overall performance of radio frequency interference identification.
射电天文学在促进科学进步和揭开宇宙奥秘方面发挥着非常重要的作用。然而,射电望远镜在接收无线电信号时不可避免地会受到射频干扰(RFI)的影响,导致数据质量下降,严重影响正确科学结论的形成。因此,识别观测数据中存在的射频干扰至关重要。为了有效识别射频干扰,改进现有存在漏检问题的射频干扰识别方法,提高射频干扰识别的性能,本文提出了一种将双交叉注意机制与多尺度特征融合相结合的新方法。利用中国科学院云南天文台 40 米射电望远镜的观测数据进行了实验研究。所提出的方法在精确度、召回率和 F1 分数方面分别达到了 92.49%、83.90% 和 87.99%。该方法在召回率和 F1 分数方面优于现有方法(U-Net、RFI-Net、R-Net6、RFI-GAN、EMSCA-UNet),有效减少了漏检的发生,提高了射频干扰识别的整体性能。
Astronomy and ComputingASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍:
Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.