首页|The RFI Fast Mitigation Algorithm Based on Block LMS Filter

The RFI Fast Mitigation Algorithm Based on Block LMS Filter

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The radio telescope possesses high sensitivity and strong signal collection capabilities.While receiving celestial radiation signals,it also captures Radio Frequency Interferences(RFIs)introduced by human activities.RFI,as signals originating from sources other than the astronomical targets,significantly impacts the quality of astronomical data.This paper presents an RFI fast mitigation algorithm based on block Least Mean Square(LMS)algorithm.It enhances the traditional adaptive LMS filter by grouping L adjacent time-sampled points into one block and applying the same filter coefficients for filtering within each block.This transformation reduces multiplication calculations and enhances algorithm efficiency by leveraging the time-domain convolution theorem.The algorithm is tested using baseband data from the Parkes 64 m radio telescope's pulsar observations and simulated data.The results confirm the algorithm's effectiveness,as the pulsar profile after RFI mitigation closely matches the original pulsar profile.

methods:data analysistechniques:interferometric(stars:)pulsars:individual(J0437-4715)

Han Wu、Hai-Long Zhang、Ya-Zhou Zhang、Jie Wang、Xu Du、Ting Zhang、Xin-Chen Ye

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Xinjiang Astronomical Observatory,Chinese Academy of Sciences,Urumqi 830011,China

University of Chinese Academy of Sciences,Beijing 100049,China

Key Laboratory of Radio Astronomy,Chinese Academy of Sciences,Nanjing 210008,China

National Astronomical Data Center,Beijing 100101,China

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国家重点研发计划国家重点研发计划国家自然科学基金国家自然科学基金Tianshan Innovation Team Plan of Xinjiang Uygur Autonomous RegionTianshan Talent Project of Xinjiang Uygur Autonomous RegionScientific Instrument Developing Project of the Chinese Academy of SciencesChina National Astronomical Data Center(NADC)Operation,Maintenance and Upgrading Fund for Astronomical Telescopes and Facility Instruments,budgeted from the Ministry of 中国科学院项目新疆维吾尔自治区自然科学基金"西部之光"人才培养计划Astronomical Big Data Joint Research CenterNational Astronomical Observatories,Chinese Academy of Sciences

2021YFC22035022022YFF071150212173077120730672022D140202022TSYCCX0095PTYQ2022YZZD012022D01A3602022-XBQNXZ-012

2024

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

CSTPCD
影响因子:0.406
ISSN:1674-4527
年,卷(期):2024.24(1)
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