基于支撑集保护的回环匹配算法

田淑娟; 樊晓平; 裴廷睿; 杨术; 李哲涛

doi:10.11959/j.issn.1000-436x.2015243

您当前的位置：

首页 >

文章列表页 >

基于支撑集保护的回环匹配算法

学术论文 | 更新时间：2024-06-05

- 基于支撑集保护的回环匹配算法
- Loopback matching algorithm with support set protection
- 通信学报 2015年36卷第9期页码：127-134
- 作者机构：
  
  1. 中南大学信息科学与工程学院，湖南长沙 410075
  2. 湘潭大学信息工程学院，湖南湘潭 411105
  3. 湖南财政经济学院网络化系统研究所，湖南长沙 410205
- 作者简介：
  
  [ "田淑娟（1982-），女，湖南攸县人，中南大学博士生，主要研究方向为压缩感知和无线传感器网络。" ]
  [ "樊晓平（1961-），男，浙江绍兴人，博士，" ]
  [ "裴廷睿（1970-），男，湖南通道人，博士，湘潭大学教授、博士生导师，主要研究方向为无线传感器网络、多媒体通信。" ]
  [ "杨术（1988-），男，湖南长沙人，湘潭大学硕士生，主要研究方向为无线传感器网络、压缩感知。" ]
  [ "李哲涛（1980-），男，湖南邵阳人，博士，湘潭大学副教授、硕士生导师，主要研究方向为无线传感器网络、压缩感知。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61379115);国家自然科学基金资助项目(61110215);国家自然科学基金资助项目(61311140261);国家自然科学基金资助项目(61372049);湖南省自然科学基金资助项目(2015JJ4047);湖南省自然科学基金委员会与湘潭市政府自然科学联合基金资助项目(12JJ9021);湖南省自然科学基金资助项目(13JJ8006);湖南省科技计划基金资助项目(2014GK3038);湖南省重点学科建设基金资助项目
- DOI：10.11959/j.issn.1000-436x.2015243
  中图分类号： TN911
- 网络出版日期：2015-09，
  
  纸质出版日期：2015-09-25
- 稿件说明：
移动端阅览
田淑娟, 樊晓平, 裴廷睿, 等. 基于支撑集保护的回环匹配算法[J]. 通信学报, 2015,36(9):127-134.

Shu-juan TIAN, Xiao-ping FAN, Ting-rui PEI, et al. Loopback matching algorithm with support set protection[J]. Journal on communications, 2015, 36(9): 127-134.
田淑娟, 樊晓平, 裴廷睿, 等. 基于支撑集保护的回环匹配算法[J]. 通信学报, 2015,36(9):127-134. DOI： 10.11959/j.issn.1000-436x.2015243.

Shu-juan TIAN, Xiao-ping FAN, Ting-rui PEI, et al. Loopback matching algorithm with support set protection[J]. Journal on communications, 2015, 36(9): 127-134. DOI： 10.11959/j.issn.1000-436x.2015243.

摘要

针对部分压缩感知贪婪迭代类重构算法中误删正确支撑集元素的缺点，提出了一种基于支撑集保护的回环匹配算法（LM-P）。该算法依据最小残差内积初始化非受保护支撑集元素，然后依据观测向量在非受保护支撑集对应观测子矩阵上的投影，选择对应投影绝对值最大的元素添加到受保护支撑集，迭代获得受保护支撑集，从而重构原始信号。实验结果表明，对于非零值服从正态分布且稀疏度小于观测值一半数目的稀疏信号，LM-P算法的重构准确率超过86%；对于低信噪比稀疏信号，该算法的重构准确率能够维持在99%以上；与OMP、CoSaMP、SP和 GPA算法相比，LM-P精确重构所需观测值数更少；此外，LM-P算法在二维图像信号的重构中也有较好性能。

Abstract

There was a drawback of deleting right support elements in some greedy iterative reconstruction algorithms.To resolve this problem

loopback matching algorithm with support set protection (LM-P) was proposed.First

LM-P ini-tialized elements of non-protected support set based on minimum residual inner product.Second

it computed the projec-tions of observations on the observation sub-matrix corresponding to non-protected support set elements.Then

an ele-ment in non-protected support set with the largest projection was added to the protected support set.An alternative multi-plicative iteration method was employed to obtain the whole protected support set.As to reconstruct a sparse signal whose nonzero elements are normally distributed and the signal sparsity is less than half the number of measurements

experimental results show that the reconstruction accuracy of LM-P algorithm exceeds 86%.For sparse signals with small noise

the reconstruction accuracy of LM-P can maintain over 99 %.Compared with OMP

CoSaMP

SP and GPA algo-rithms

LM-P's observations are smaller.LM-P also has good performance for image reconstruction.greedy iteration;support set;sparse signal;LM-P

关键词

Keywords

references

DONOHO D L . Compressed sensing [J ] . IEEE Trans on Information Theory , 2006 , 52 ( 4 ): 1289 - 1306 .

CANDÈS E , WAKIN M . An introduction to compressive sampling [J ] . IEEE Signal Processing Magazine , 2008 , 25 ( 2 ): 21 - 30 .

QAISAR S , BILAL R , IQBAL W , et al . Compressive sensing:from theory to applications,a survey [J ] . Communications and Networks , 2013 , 15 ( 5 ): 443 - 456 .

JIAO L C , YANG S Y , LIU F , et al . Development and prospect of compressive sensing [J ] . Acta Electronica Sinica , 2011 , 39 ( 1 ): 18 - 22 .

ZHAO C H , XU Y L . Energy constraint Bayesian compressive sensing detection algorithm [J ] . Journal on Communications , 2012 , 33 ( 10 ): 1 - 6 .

WANG W W , LIAO G S , ZHANG L . An imaging method based on compressive sensing for sparse aperture of SAR [J ] . Acta Electronica Sinica , 2012 , 40 ( 12 ): 2487 - 2494 .

HU Y , ONGIE G , RAMANI S , et al . Generalized higher degree total variation (HDTV) regularization [J ] . IEEE Transactions on Image Processing , 2014 , 23 ( 6 ): 2423 - 2435 .

PEJOSKI S , KAFEDZISKI V , GLEICH D . Compressed sensing MRI using discrete nonseparable shearlet transform and FISTA [J ] . Signal Processing Letters,IEEE , 2015 , 22 ( 10 ): 1566 - 1570 .

PEI T R , YANG S , LI Z T . Detouring matching pursuit algorithm in compressed sensing [J ] . Journal of Computer Research and Develop-ment , 2014 , 51 ( 9 ): 2101 - 2107 .

TROPP J , GILBERT A . Signal recovery from random measurements via orthogonal matching pursuit [J ] . IEEE Trans on Information The-ory , 2007 , 53 ( 12 ): 4655 - 4666 .

DONOHO D L , TSAIG Y , DRORI I , et al . Sparse solution of under-determined systems of linear equations by stagewise orthogonal matching pursuit [J ] . IEEE Transactions on Information Theory , 2012 , 58 ( 2 ): 1094 - 1121 .

SAHOO S K , MAKUR A . Signal recovery from random measure-ments via extended orthogonal matching pursuit [J ] . IEEE Transactions on Signal Processing , 2015 , 63 ( 10 ): 2572 - 2581 .

NEEDELL D , TROPP J . Iterative signal recovery from incomplete and inaccurate samples [J ] . Applied and Computational Harmonic Analysis , 2009 , 26 ( 3 ): 302 - 321 .

DAI W , MILENKOVIC O . Subspace pursuit for compressive sensing signal reconstruction [J ] . IEEE Trans on Information Theory , 2009 , 55 ( 5 ): 2230 - 2249 .

VARADARAJAN B , KHUDANPUR S . Stepwise optimal subspace pursuit for improving sparse recovery [J ] . IEEE Signal Processing Let-ters , 2011 , 18 ( 1 ): 27 - 30 .

浏览量

886

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据