首页|DSC based Dual-Resunet for radio frequency interference identification

DSC based Dual-Resunet for radio frequency interference identification

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Radio frequency interference(RFI)will pollute the weak astronomical signals received by radio telescopes,which in return will seriously affect the time-domain astronomical observation and research.In this paper,we use a deep learning method to identify RFI in frequency spectrum data,and propose a neural network based on Unet that combines the principles of depthwise separable convolution and residual,named DSC Based Dual-Resunet.Compared with the existing Unet network,DSC Based Dual-Resunet performs better in terms of accuracy,F1 score,and MIoU,and is also better in terms of computation cost where the model size and parameter amount are 12.5％of Unet and the amount of computation is 38％of Unet.The experimental results show that the proposed network is a high-performance and lightweight network,and it is hopeful to be applied to RFI identification of radio telescopes on a large scale.

techniques:deep learning and image processingradio frequency interferencetelescopesSun:radio radiation

Yan-Jun Zhang、Yan-Zuo Li、Jun Cheng、Yi-Hua Yan

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School of Cyberspace Science and Technology,Beijing Institute of Technology,Beijing 100081,China

School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China

CAS Key Laboratory of Solar Activity,National Astronomical Observatories,Beijing 100101,China

State Key Laboratory of Space Weather,Chinese Academy of Sciences,Beijing 100864,China

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11790305SYS-202002-04

2021

10.1088/1674-4527/ac2944

天文和天体物理学研究
中国科学院国家天文台

天文和天体物理学研究

CSTPCDCSCDSCI
影响因子：0.406
ISSN：1674-4527
年,卷(期)：2021.21(12)
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