Forward-backward pursuit method for distributed compressed sensing

被引:0
作者
Yujie Zhang
Rui Qi
Yanni Zeng
机构
[1] China University of Geosciences,School of Mathematics and Physics
[2] University of Windsor,School of Computer Science
[3] Naval University of Engineering,School of Science
[4] Hubei University of Economics,Faculty of Statistics
来源
Multimedia Tools and Applications | 2017年 / 76卷
关键词
Distributed compressed sensing; Forward-backward pursuit; Sparsity; Sparse signal reconstruction;
D O I
暂无
中图分类号
学科分类号
摘要
In this paper, a forward-backward pursuit method for distributed compressed sensing (DCSFBP) is proposed. In contrast to existing distributed compressed sensing (DCS), it is an adaptive iterative approach where each iteration consists of consecutive forward selection and backward removal stages. And it not needs sparsity as prior knowledge and multiple indices are identified at each iteration for recovery. These make it a potential candidate for many practical applications, when the sparsity of signals is not available. Numerical experiments, including recovery of random sparse signals with different nonzero coefficient distributions in many scenarios, in addition to the recovery of sparse image and the real-life electrocardiography (ECG) data, are conducted to demonstrate the validity and high performance of the proposed algorithm, as compared to other existing DCS algorithms.
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页码:20587 / 20608
页数:21
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