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基于非凸优化模型的块稀疏信号恢复条件

周珺 黄尉

周珺, 黄尉. 基于非凸优化模型的块稀疏信号恢复条件[J]. 应用数学和力学, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154
引用本文: 周珺, 黄尉. 基于非凸优化模型的块稀疏信号恢复条件[J]. 应用数学和力学, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154
ZHOU Jun, HUANG Wei. Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model[J]. Applied Mathematics and Mechanics, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154
Citation: ZHOU Jun, HUANG Wei. Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model[J]. Applied Mathematics and Mechanics, 2019, 40(2): 167-180. doi: 10.21656/1000-0887.390154

基于非凸优化模型的块稀疏信号恢复条件

doi: 10.21656/1000-0887.390154
基金项目: 国家自然科学基金重大研究计划(91538112); 国家自然科学基金青年科学基金(11201450)
详细信息
    作者简介:

    周珺(1994—),女,硕士生(E-mail: 1812253174@qq.com);黄尉(1977—),男,教授,博士,硕士生导师(通讯作者. E-mail: whuang@hfut.edu.cn).

  • 中图分类号: O174.2

Improved Conditions for Block-Sparse Signal Recovery via the Non-Convex Optimization Model

Funds: The Major Research Plan of the National Natural Science Foundation of China(91538112); The National Science Fund for Young Scholars of China(11201450)
  • 摘要: 压缩感知(compressed sensing,CS)是一种全新的信息采集与处理理论,它表明稀疏信号能够在远低于Shannon-Nyquist采样率的条件下被精确重构.现从压缩感知理论出发,对块稀疏信号重构算法进行研究,通过混合l2/lq(0
  • [1] DONOHO D. Compressed sensing[J]. IEEE Transactions on Information Theory,2006,52(4): 1289-1306.
    [2] CANDES E J, ROMBERG J, TAO T. Stable signal recovery from incomplete and inaccurate measurements[J]. Communications Pure and Applied Mathematics,2006,59(8): 1207-1223.
    [3] CANDES E J. The restrictedisometry property and its implications for compressed sensing[J]. Comptes Rendus Mathematique,2008,346(9/10): 589-592.
    [4] CAI T, WANG L, XU G W. New bounds for restricted isometry constants[J]. IEEE Transactions on Information Theory,2010,56(9): 4388-4394.
    [5] CAI T, ZHANG A R. Compressed sensing and affine rank minimization under restricted isometry[J]. IEEE Transactions on Signal Processing,2013,61(13): 3279-3290.
    [6] FOUCART S. A note on guaranteed sparse recovery via l1-minimization[J]. Applied and Computational Harmonic Analysis,2010,29(1): 97-103.
    [7] DAVIES M, GRIBONVAL R. Restricted isometry constants where lp sparse recovery can fail for 0
    [8] LUSTIG M, DONOHO D L, PAULY J M. Rapid MR imaging with compressed sensing and randomly under-sampled 3DFT trajectories[C]// Proceeding of the 〖STBX〗14th Annual Meeting of ISMRM. Seattle, USA, 2006.
    [9] DUARTE M, DAVENPORT M, TAKBAR D, et al. Single-pixel imaging via compressive sampling[J]. IEEE Signal Processing Magazine,2008,25(2): 83-91.
    [10] BARANIUK R, STEEGHS P. Compressive radar imaging[C]// Proceeding of the IEEE Radar Conference . Washington DC, USA, 2007.
    [11] BAJWA W, HAUPT J, SAYEED A, et al. Joint source-channel communication for distributed estimation in sensor networks[J]. IEEE Transactions on Information Theory,2007,53(10): 3629-3653.
    [12] ELDER Y, MISHALI M. Robust recovery of signals from a structured union of subspaces[J]. IEEE Transactions on Information Theory,2009,55(11): 5302-5316.
    [13] MISHALI M, ELDAR Y. Blind multiband signalreconstruction: compressed sensing for analog signals[J]. IEEE Transactions on Signal Processing,2009,57(3): 993-1009.
    [14] DAI W,SHEIKH M A, MILENKOVIC O, et al. Compressed sensing DNA microarrays[J]. EURASIP Journal on Bioinformatics and Systerms Biology,2009,2009(1): 162824.
    [15] ENDER J. On compressive sensing applied to radar[J]. Signal Processing,2010,90(5): 1402-1414.
    [16] YANG Z, XIE L. Continuous compressed sensing with a single or multiple measurement vectors[C]//2014 IEEE Workshop on Statistical Signal Processing(SSP),2014: 308-311. DOI: 10.1109/SSP.2014.6884632.
    [17] LIN J H, LI S. Block sparse recovery via mixed l2/l1 minimization[J]. Acta Mathematica Sinica,2013,29(7): 1401-1412.
    [18] CHEN W, LI Y. The high order block RIP condition for signal recovery[J]. Journal of Computational Mathematics,2016,37(1): 61-75.
    [19] ZHOU Shenglong, KONG Lingchen, LUO Ziyan, et al. New RIC bounds via lq-minimization with 0
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出版历程
  • 收稿日期:  2018-05-25
  • 修回日期:  2018-12-04
  • 刊出日期:  2019-02-01

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