Support driven recovery algorithm for non-convex compressed sensing

被引：0
|
作者：
Wang F. [1 ,2 ]
Xiang X. [2 ]
Yi K. [1 ]
Xiong L. [2 ]
机构：
[1] State Key Lab. of Integrated Service Networks, Xidian Univ., Xi'an
[2] Aeronautics and Astronautics Engineering College, Air Force Engineering Univ., Xi'an
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2016年 / 43卷 / 02期
关键词：
Basis pursuit; Compressed sensing; Iteratively reweighted Lp minimization;
D O I：
10.3969/j.issn.1001-2400.2016.02.001
中图分类号：
学科分类号：
摘要：
A novel method is presented for the purpose of recovering sparse high dimensional signals from few linear measurements, especially in the noisy case. The proposed method works in the following two steps: The support of signal is approximately identified via Thresholded Basis Pursuit(TBP), the weighting matrix and parameters needed for the next step are also computed; The Iteratively Reweighted Lp Minimization(IRLp) procedure is used to solve the non-convex objective function. As theoretic interpretation and simulation results show, lower computational complexity is required for the proposed Support Driven IRLp(SD-IRLp) algorithm for high probability recovery, in comparison to 7 analogous methods(including an oracle estimator). © 2016, Science Press. All right reserved.
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页码：1 / 5and28
页数：527
相关论文
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