Radio frequency interference mitigation using pseudoinverse learning autoencoders

被引：7

作者：

Wang, Hong-Feng ^{[1
,2
,3
,5
]}

Yuan, Mao ^{[2
,6
]}

Yin, Qian ^{[1
]}

Guo, Ping ^{[4
]}

Zhu, Wei-Wei ^{[2
]}

Li, Di ^{[2
,6
,8
]}

Feng, Si-Bo ^{[7
]}

机构：

[1] Beijing Normal Univ, Sch Artificial Intelligence, Image Proc & Pattern Recognit Lab, Beijing 100875, Peoples R China

[2] Chinese Acad Sci, Natl Astron Observ, CAS Key Lab FAST, Beijing 100101, Peoples R China

[3] Dezhou Univ, Sch Informat Management, Dezhou 253023, Peoples R China

[4] Beijing Normal Univ, Sch Syst Sci, Image Proc & Pattern Recognit Lab, Beijing 100875, Peoples R China

[5] Dezhou Univ, Inst Astron Sci, Dezhou 253023, Peoples R China

[6] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[7] Hanvon Technol Co Ltd, Beijing 100193, Peoples R China

[8] Univ KwaZulu Natal, NAOC UKZN Computat Astrophys Ctr, ZA-4000 Durban, South Africa

来源：

RESEARCH IN ASTRONOMY AND ASTROPHYSICS | 2020年 / 20卷 / 08期

基金：

中国国家自然科学基金;

关键词：

pulsars; general; methods; numerical; data analysis; CLASSIFICATION; TELESCOPE; SOFTWARE; REMOVAL; ARRAYS;

D O I：

10.1088/1674-4527/20/8/114

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

Radio frequency interference (RFI) is an important challenge in radio astronomy. RFI comes from various sources and increasingly impacts astronomical observation as telescopes become more sensitive. In this study, we propose a fast and effective method for removing RFI in pulsar data. We use pseudo-inverse learning to train a single hidden layer auto-encoder (AE). We demonstrate that the AE can quickly learn the RFI signatures and then remove them from fast-sampled spectra, leaving real pulsar signals. This method has the advantage over traditional threshold-based filter method in that it does not completely remove contaminated channels, which could also contain useful astronomical information.

引用

页数：8

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