A new backtracking-based sparsity adaptive algorithm fordistributed compressed sensing
来源期刊：中南大学学报(英文版)2015年第10期
论文作者：XU Yong ZHANG Yu-Jie XING Jing LI Hong-wei
文章页码：3946 - 3956
Key words：distributed compressed sensing; sparsiy; backtracking; soft thresholding
Abstract: A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but 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, as compared to other existing strong DCS algorithms.
XU Yong(徐勇)1, 2, 3 , ZHANG Yu-Jie(张玉洁)1, XING Jing(邢婧)1, 2, 3, LI Hong-wei(李宏伟)1, 3
(1. School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China;
2. Institute of Statistics, Hubei University of Economics, Wuhan 430205, China;
3. Hubei Subsurface Multi-scale Imaging Key Laboratory, China University of Geosciences, Wuhan 430074, China)
Abstract:A new iterative greedy algorithm based on the backtracking technique was proposed for distributed compressed sensing (DCS) problem. The algorithm applies two mechanisms for precise recovery soft thresholding and cutting. It can reconstruct several compressed signals simultaneously even without any prior information of the sparsity, which makes it a potential candidate for many practical applications, but 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, as compared to other existing strong DCS algorithms.
Key words:distributed compressed sensing; sparsiy; backtracking; soft thresholding
