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基于图谱域移位的带限图信号重构算法

杨杰 赵磊 郭文彬

杨杰, 赵磊, 郭文彬. 基于图谱域移位的带限图信号重构算法. 自动化学报, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
引用本文: 杨杰, 赵磊, 郭文彬. 基于图谱域移位的带限图信号重构算法. 自动化学报, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
Yang Jie, Zhao Lei, Guo Wen-Bin. Graph band-limited signals reconstruction method based graph spectral domain shifting. Acta Automatica Sinica, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802
Citation: Yang Jie, Zhao Lei, Guo Wen-Bin. Graph band-limited signals reconstruction method based graph spectral domain shifting. Acta Automatica Sinica, 2021, 47(9): 2132−2142 doi: 10.16383/j.aas.c200802

基于图谱域移位的带限图信号重构算法

doi: 10.16383/j.aas.c200802
基金项目: 国家自然科学基金(61271181) 资助
详细信息
    作者简介:

    杨杰:北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为图信号处理. E-mail: yjie934@bupt.edu.cn

    赵磊:北京邮电大学信息与通信工程学院博士研究生. 主要研究方向为无线信号处理. E-mail: leizhao@bupt.edu.cn

    郭文彬:北京邮电大学信息与通信工程学院教授. 主要研究方向为无线信号处理. 本文通信作者. E-mail: gwb@bupt.edu.cn

Graph Band-limited Signals Reconstruction Method Based Graph Spectral Domain Shifting

Funds: Supported by National Natural Science Foundation of China (61271181)
More Information
    Author Bio:

    YANG Jie Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is graph signal processing

    ZHAO Lei Ph.D. candidate at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing

    GUO Wen-Bin Professor at the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications. His main research interest is wireless signal processing. Corresponding author of this paper

  • 摘要: 针对带限图信号的重构问题, 本文提出了基于图谱域移位的带限图信号重构模型, 该模型将图带限分量的恒等不变特性建模为最小二乘问题. 基于所提出的重构模型, 本文设计了基于谱移位的重构算法和基于残差谱移位的重构算法. 相比于其他重构算法, 两种新算法提升了迭代效率和重构精度. 此外, 本文算法还适用于分段带限图信号的重构问题, 并且具有良好的迭代效率和重构精度.通过实验仿真表明, 相比于目前其他的带限图信号重构算法, 新算法的迭代效率提升约70%和重构精度提升约60%.
  • 图  1  带限图信号

    Fig.  1  Graph band-limited signals

    图  2  分段带限图信号

    Fig.  2  Graph sperate band-limited signals

    图  3  图信号采样

    Fig.  3  Graph signals sampling

    图  4  无噪环境下带限图信号重构性能对比

    Fig.  4  Comparison of graph band-limited signals reconstruction performances in noiseless environment

    图  5  含噪环境下带限图信号重构性能对比

    Fig.  5  Comparison of graph band-limited signals reconstruction performances in noisy environment

    图  6  分段带限图信号重构性能对比

    Fig.  6  Comparison of graph separate band-limited signals reconstruction performances

    表  1  无噪情况下基于随机采样的${G_1}$重构效率

    Table  1  ${G_1}$ reconstruction efficiency of random sampling in noiseless

    算法迭代次数运行时间 (s)
    ILSR 220 139.99
    OPGIR 114 108.78
    IPR 96 61.87
    IGDR 33 20.47
    BGSR-GFS 27 5.73
    BGSR-GFS-R 8 8.97
    下载: 导出CSV

    表  2  无噪情况下基于随机采样的${G_2}$重构效率

    Table  2  ${G_2}$ reconstruction efficiency of random sampling in noiseless

    算法迭代次数运行时间 (s)
    ILSR 269 0.1509
    OPGIR 139 0.1291
    IPR 64 0.0405
    IGDR 34 0.0271
    BGSR-GFS 7 0.0065
    BGSR-GFS-R 5 0.0146
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-09-28
  • 录用日期:  2021-01-26
  • 网络出版日期:  2021-04-28
  • 刊出日期:  2021-10-13

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