GENERALIZED ORTHOGONAL MATCHING PURSUIT FOR DISTRIBUTED COMPRESSED SENSING

被引：0
|
作者：
Xu, Yong [1 ,2 ,3 ]
Xing, Jing [1 ,2 ,3 ]
Zhang, Yujie [1 ]
Li, Hongwei [1 ,2 ]
机构：
[1] China Univ Geosci, Sch Math & Phys, 388,Lumo Rd, Wuhan 430074, Hubei, Peoples R China
[2] China Univ Geosci, Hubei Subsurface Multiscale Imaging Key Lab, Wuhan 430074, Hubei, Peoples R China
[3] Hubei Univ Econ, Inst Stat, Wuhan 430205, Hubei, Peoples R China
来源：
INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL | 2015年 / 11卷 / 04期
关键词：
Distributed compressed sensing; Orthogonal matching pursuit; Sparsity; Correlation;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, generalized orthogonal matching pursuit for distributed compressed sensing (DCS-COMP) is proposed. As its name, the algorithm is based on orthogonal matching pursuit (OMP), but not need sparsity as prior knowledge and multiple indices are identified at each iteration for recovery. These make it a potential candidate for many practical applications, where the numbers of non-zero (significant) coefficients of signals are not available. Numerical experiments are conducted to demonstrate the validity and high performance of the proposed algorithm, compared with other existing DCS algorithms.
引用
收藏
页码：1441 / 1456
页数：16
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