A Tree-based Regularized Orthogonal Matching Pursuit Algorithm

被引：0
|
作者：
Li, Zhilin [1 ]
Xu, Wenbo [1 ]
Wang, Yue [2 ]
Lin, Jiaru [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing 100095, Peoples R China
[2] Huawei Technol Co Ltd, Res Dept Hisilicon, Beijing 100095, Peoples R China
来源：
2015 22ND INTERNATIONAL CONFERENCE ON TELECOMMUNICATIONS (ICT) | 2015年
关键词：
Compressed sensing; regularized orthogonal matching pursuit; sparse tree structure; prior information; ratio factor; SIGNAL RECOVERY;
D O I：
暂无
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：
Reconstruction algorithm is a significant research field of compressed sensing (CS). Among existing algorithms, regularized orthogonal matching pursuit (ROMP) enjoys the merit of implementing fast recovery procedures. Recent studies have recognized that sparse signals have special sparse structure, which is useful for reconstruction as prior information. In this paper, by utilizing the sparse tree structure as prior information, we propose a tree-based regularized orthogonal matching pursuit (T-ROMP) reconstruction algorithm. Furthermore, we set a ratio factor to reduce the error probability of the support set. Compared to ROMP, simulation results indicate that the proposed algorithm achieve better reconstruction performance for different conditions.
引用
收藏
页码：343 / 347
页数：5
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