NAOC Open IR
Detection of radio frequency interference using an improved generative adversarial network
Li, Z.1; Yu, C.1; Xiao, J.1; Long, M.2; Cui, C.3
2021-07-01
Source PublicationASTRONOMY AND COMPUTING
ISSN2213-1337
Volume36Pages:8
AbstractRadio Frequency Interference (RFI) is a type of inevitable noise in the radio astronomy data collection process. It can corrupt weak cosmic signals and potentially lead to misleading results. The proper identification of RFI during data processing thus is critical to obtain clean and high-quality data for analysis. This need will become even more urgent when next generation of large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST) comes into service and generates an increasing amount and complexity of radio signal data. Among RFI identification methods, detection using artificial intelligence (AI) has particularly demonstrated advantages in superior efficiency, accuracy and less human intervention. We thus propose a RFI detection model based on Pix2Pix, an image-to-image translation solver using Generative Adversarial Network (GAN): RFI-GAN. This model transforms the RFI detection to an image translation problem and trains two deep neural networks that contest each other to output a binary RFI mask image to eliminate RFI noises. We also optimize the network structures of the generator and discriminator used in the Pix2Pix model for better quality of RFI detection, making it suitable for processing data from single antenna radio telescope. The model is designed to serve the upcoming FAST data and has been evaluated using a standard simulation data set generated by the HI Data Emulator (HIDE). Experimental results have shown that our model can achieve higher scores (99%) on accuracy, recall and Fl-score than other RFI detection methods. (C) 2021 Elsevier B.V. All rights reserved.
KeywordRFI detecting GAN Image processing
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China
DOI10.1016/j.ascom.2021.100482
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[U1731125] ; National Natural Science Foundation of China (NSFC)[U1731243] ; National Natural Science Foundation of China (NSFC)[U1931130] ; Chinese Academy of Sciences (CAS)[U1731125] ; Chinese Academy of Sciences (CAS)[U1731243] ; Chinese Academy of Sciences (CAS)[U1931130] ; National Natural Science Fund of China[11803022] ; National Natural Science Fund of China[11573019]
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China ; National Natural Science Foundation of China (NSFC) ; National Natural Science Foundation of China (NSFC) ; Chinese Academy of Sciences (CAS) ; Chinese Academy of Sciences (CAS) ; National Natural Science Fund of China ; National Natural Science Fund of China
WOS Research AreaAstronomy & Astrophysics ; Computer Science
WOS SubjectAstronomy & Astrophysics ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000691531100018
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/75462
Collection中国科学院国家天文台
Corresponding AuthorXiao, J.; Long, M.
Affiliation1.Tianjin Univ, Coll Intelligence & Comp, 135 Yaguan Rd,Haihe Educ Pk, Tianjin 300350, Peoples R China
2.Boise State Univ, Dept Comp Sci, Boise, ID 83725 USA
3.Chinese Acad Sci, Natl Astron Observ, 20 Datun Rd, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Li, Z.,Yu, C.,Xiao, J.,et al. Detection of radio frequency interference using an improved generative adversarial network[J]. ASTRONOMY AND COMPUTING,2021,36:8.
APA Li, Z.,Yu, C.,Xiao, J.,Long, M.,&Cui, C..(2021).Detection of radio frequency interference using an improved generative adversarial network.ASTRONOMY AND COMPUTING,36,8.
MLA Li, Z.,et al."Detection of radio frequency interference using an improved generative adversarial network".ASTRONOMY AND COMPUTING 36(2021):8.
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