Performance guarantees of signal recovery via block-OMP with thresholding

被引:1
|
作者
Hu, Rui [1 ]
Fu, Yuli [1 ]
Xiang, Youjun [1 ]
Rong, Rong [1 ]
机构
[1] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China
关键词
Gaussian noise; signal representation; performance guarantees; signal recovery; block sparsity; sparse signal representation; block structure; sparsity pattern; block version of the orthogonal matching pursuit with thresholding algorithm; block-OMPT algorithm; Gaussian noise case; ORTHOGONAL MATCHING PURSUIT; SIMULTANEOUS SPARSE APPROXIMATION; RESTRICTED ISOMETRY PROPERTY; ALGORITHMS; SUBSPACES; UNION;
D O I
10.1049/iet-spr.2017.0076
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Block-sparsity is an extension of the ordinary sparsity in the realm of the sparse signal representation. Exploiting the block structure of the sparsity pattern, recovery may be possible under more general conditions. In this study, a block version of the orthogonal matching pursuit with thresholding (block-OMPT) algorithm is proposed. Compared with the block version of the orthogonal matching pursuit (block-OMP), block-OMPT works in a less greedy fashion in order to improve the efficiency of the support estimation in iterations. Using the block restrict isometry property (block-RIP), some performance guarantees of block-OMPT are discussed for the bounded noise case and Gaussian noise case. A relationship between block-RIP and block-coherence is obtained. Numerical experiments are provided to illustrate the validity of the authors' main results.
引用
收藏
页码:952 / 960
页数:9
相关论文
共 50 条
  • [31] Projective Iterative Hard Thresholding Algorithm for Sparse Signal Recovery
    Zhou, Zhongao
    Sun, Tao
    Cheng, Lizhi
    PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON ESTIMATION, DETECTION AND INFORMATION FUSION ICEDIF 2015, 2015, : 244 - 247
  • [32] Sparse Signal Reconstruction via ECME Hard Thresholding
    Qiu, Kun
    Dogandzic, Aleksandar
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2012, 60 (09) : 4551 - 4569
  • [33] Guarantees of Fast Band Restricted Thresholding Algorithm for Low-Rank Matrix Recovery Problem
    Zhao, Fujun
    Peng, Jigen
    Sun, Kai
    Cui, Angang
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020 (2020)
  • [34] A thresholding algorithm for sparse recovery via Laplace norm
    Wang, Qian
    Qu, Gangrong
    Han, Guanghui
    SIGNAL IMAGE AND VIDEO PROCESSING, 2019, 13 (02) : 389 - 395
  • [35] A thresholding algorithm for sparse recovery via Laplace norm
    Qian Wang
    Gangrong Qu
    Guanghui Han
    Signal, Image and Video Processing, 2019, 13 : 389 - 395
  • [36] Theoretical results for sparse signal recovery with noises using generalized OMP algorithm
    Li, Bo
    Shen, Yi
    Rajan, Sreeraman
    Kirubarajan, Thia
    SIGNAL PROCESSING, 2015, 117 : 270 - 278
  • [37] Partial Discharge Signal Denoising Based on Wavelet Pair and Block Thresholding
    Zhou, Siyuan
    Tang, Ju
    Pan, Cheng
    Luo, Yang
    Yan, Kailai
    IEEE ACCESS, 2020, 8 (08): : 119688 - 119696
  • [38] Designing Optimal Middlebox Recovery Schemes with Performance Guarantees
    Kanizo, Yossi
    Rottenstreich, Ori
    Segall, Itai
    Yallouz, Jose
    IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2018), 2018, : 2105 - 2113
  • [39] Designing Optimal Middlebox Recovery Schemes With Performance Guarantees
    Kanizo, Yossi
    Rottenstreich, Ori
    Segall, Itai
    Yallouz, Jose
    IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2018, 36 (10) : 2373 - 2383
  • [40] Signal Selection for Oscillation Monitoring With Guarantees on Data Recovery Under Corruption
    Chatterjee, Kaustav
    Chaudhuri, Nilanjan Ray
    Stefopoulos, George
    2021 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2021,