基于独立成分分析的射频干扰信号消除方法
Radio Frequency Mitigation Using Independent Component Analysis
查看参考文献22篇
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文摘
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射电天文已成为人类研究宇宙的重要途径。但随着人类生产、生活的发展,射频干扰信号对射电天文观测的影响越来越严重,观测数据的好坏关系到科学成果的质量甚至结论的真伪。目前广泛采用基于阈值判断射频干扰,对干扰信号直接舍弃部分观测数据的方法。此类方法存在阈值确定困难、观测带宽和时间被缩减等问题。针对脉冲星观测射电信号中,各干扰信号及射电信号统计独立以及呈现出的非高斯性,利用独立成分分析对混合信号进行分解,并根据观测信号中脉冲星信号和干扰信号的分布特点识别脉冲星信号,实现干扰信号消除。使用该方法对云南天文台40 m射电望远镜接收到的脉冲星观测信号进行独立成分分析,分解出独立的射频干扰信号和脉冲星信号,消除射频干扰信号。独立成分分析法在干扰信号消除、射电信号保留及信噪比方面均取得良好效果。 |
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其他语种文摘
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Radio astronomy has become an important way to study the universe.However,with the development of human activities,radio frequency interference (RFI) has more and more serious impact on radio astronomical observation.The quality of observation is related to the quality of scientific achievements and even the authenticity of conclusions.At present,RFI detection based on threshold is widely used,and part of the observed data is directly discarded for the interference.Such methods have difficulties in determining threshold values,and reduce observation bandwidth and time.Observed the fact that interferences and radio signals are statistically independent and non-Gaussian,we propose a novel approach for RFI mitigation using independent component analysis to decompose the mixed signal,then identifying the pulsar signal according to the different distribution characteristics between the pulsar signal and RFI signals.The pulsar observations received from 40-meter radio telescope in Yunnan Observatories are processed by the new approach.The results show:RFI signals in pulsar observations are cleanly mitigated while pulsar signal is barely affected and good signal-to-noise ratio is achieved. |
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来源
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天文研究与技术
,2019,16(3):268-277 【核心库】
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关键词
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射频干扰
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独立成分分析
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脉冲星
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干扰信号消除
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地址
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中国科学院云南天文台, 云南, 昆明, 650011
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昆明理工大学, 云南省计算机技术应用重点实验室, 云南, 昆明, 650500
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中国科学院大学, 北京, 100049
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昆明理工大学信息工程与自动化学院, 云南, 昆明, 650500
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西南林业大学大数据与智能工程学院, 云南, 昆明, 650224
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1672-7673 |
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学科
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天文学 |
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基金
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国家自然科学基金
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云南省重点研发计划项目
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云南省应用基础研究计划项目
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文献收藏号
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CSCD:6533026
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参考文献 共
22
共2页
|
|
1.
Akeret J. Radio frequency interference mitigation using deep convolutional neural networks.
Astronomy & Computing,2017,18:35-39
|
CSCD被引
9
次
|
|
|
|
|
2.
Blandford R D. Pulsars and physics.
Philosophical Transactions of the Royal Society B Biological Sciences,1992,341(1660):177-192
|
CSCD被引
1
次
|
|
|
|
|
3.
Ransom S M. Pulsars are cool. seriously.
Proceedings of the International Astronomical Union,2012,8(Suppl 291):3-10
|
CSCD被引
1
次
|
|
|
|
|
4.
Wolszczan A. A planetary system around the millisecond pulsar PSR1257+12.
Nature,1992,355(6356):145-147
|
CSCD被引
34
次
|
|
|
|
|
5.
Taylor J H. Binary pulsars and relativistic gravity.
Reviews of Modern Physics,1994,66(3):711-719
|
CSCD被引
5
次
|
|
|
|
|
6.
Offringa A R. Post-correlation radio frequency interference classification methods.
Monthly Notices of the Royal Astronomical Society,2010,405(1):155-167
|
CSCD被引
6
次
|
|
|
|
|
7.
Fridman P A. RFI mitigation methods in radio astronomy.
Astronomy & Astrophysics,2001,378(1):327-344
|
CSCD被引
17
次
|
|
|
|
|
8.
安涛. 射电频率干扰的消减.
天文学报,2017,58(5):18-39
|
CSCD被引
3
次
|
|
|
|
|
9.
Baan W A. Radio frequency interference mitigation at the Westerbork Synthesis Radio Telescope: algorithms,test observations,and system implementation.
The Astronomical Journal,2004,128(2):933-949
|
CSCD被引
5
次
|
|
|
|
|
10.
Akeret J. HIDE & SEEK: end-to-end packages to simulate and process radio survey data.
Astronomy & Computing,2017,18:8-17
|
CSCD被引
1
次
|
|
|
|
|
11.
Cendes Y.
LOFAR observations of Swift J1644 + 57 and implications for short-duration transients,2018
|
CSCD被引
1
次
|
|
|
|
|
12.
Wolfaardt C J.
Machine learning approach to radio frequency interference (RFI) classification in radio astronomy,2016
|
CSCD被引
2
次
|
|
|
|
|
13.
Bethapudi S. Separation of pulsar signals from noise using supervised machine learning algorithms.
Astronomy & Computing,2018,23:15(14pp)
|
CSCD被引
4
次
|
|
|
|
|
14.
Cardoso J F. Blind signal separation: statistical principles.
Proceedings of the IEEE,2009,86(10):2009-2025
|
CSCD被引
71
次
|
|
|
|
|
15.
Hyvarinen A.
What is independent component analysis?,2003:145-164
|
CSCD被引
1
次
|
|
|
|
|
16.
Huber P J. Projection pursuit.
Annals of Statistics,1985,13(2):435-475
|
CSCD被引
46
次
|
|
|
|
|
17.
Cover T M.
Elements of information theory (Wiley series in telecommunications and signal processing),2017
|
CSCD被引
1
次
|
|
|
|
|
18.
Hyvarinen A. Independent component analysis: algorithms and applications.
Neural Networks,2000,13(4):411-430
|
CSCD被引
490
次
|
|
|
|
|
19.
Hyvarinen A. New approximations of differential entropy for independent component analysis and projection pursuit.
Advances in Neural Information Processing Systems,1997,10:273-279
|
CSCD被引
3
次
|
|
|
|
|
20.
Hyvarinen A. Fast and robust fixed-point algorithms for independent component analysis.
IEEE Transactions on Neural Networks,1999,10(3):626-634
|
CSCD被引
571
次
|
|
|
|
|
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