基于联合稀疏模型的无线传感网数据重构算法

刘义颖; 李国瑞; 田丽

doi:10.11959/j.issn.1000-436x.2016269

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基于联合稀疏模型的无线传感网数据重构算法

学术通信 | 更新时间：2024-06-05

- 基于联合稀疏模型的无线传感网数据重构算法
- Joint sparse model based data reconstruction algorithm for wireless sensor network
- 通信学报 2016年37卷第Z1期页码：211-218
- 作者机构：
  
  东北大学计算机科学与工程学院，辽宁沈阳 110819
- 作者简介：
  
  [ "刘义颖（1991-），女，河北保定人，东北大学硕士生，主要研究方向为无线传感器网络、压缩感知。" ]
  [ " 李国瑞（1980-），男，山西夏县人，博士，东北大学副教授、硕士生导师，主要研究方向为无线传感器网络、压缩感知。" ]
  [ "田丽（1991-），女，河北唐山人，东北大学硕士生，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(61402094);辽宁省自然科学基金资助项目(201602254);河北省自然科学基金资助项目(F2016501076)
- DOI：10.11959/j.issn.1000-436x.2016269
  中图分类号： TP393
- 网络出版日期：2016-10，
  
  纸质出版日期：2016-10-25
- 稿件说明：
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刘义颖, 李国瑞, 田丽. 基于联合稀疏模型的无线传感网数据重构算法[J]. 通信学报, 2016,37(Z1):211-218.

Yi-yin LIU, Guo-rui LI, Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on communications, 2016, 37(Z1): 211-218.
刘义颖, 李国瑞, 田丽. 基于联合稀疏模型的无线传感网数据重构算法[J]. 通信学报, 2016,37(Z1):211-218. DOI： 10.11959/j.issn.1000-436x.2016269.

Yi-yin LIU, Guo-rui LI, Li TIAN. Joint sparse model based data reconstruction algorithm for wireless sensor network[J]. Journal on communications, 2016, 37(Z1): 211-218. DOI： 10.11959/j.issn.1000-436x.2016269.

摘要

无线传感器网络中数据具有较强联合稀疏特性，应用压缩感知理论，通过联合编码压缩数据，再使用联合解码进行还原，可实现低采样代价收集传感数据。提出了一种基于联合稀疏模型与压缩感知理论的同步子空间追踪算法，以稀疏特性为先验知识，通过回溯迭代方式，判断并选取合适的联合子空间，用更少量观测值实现原始传感数据的精确重构。与SCoSaMP算法、SP算法在不同稀疏特性和不同采样率下相比较，同步子空间追踪算法具有较好的恢复性能。

Abstract

The data of wireless sensor network has strong joint sparse characteristics

by utilizing compressed sensing theory

compressed data by joint encoding

and then reconstructed the data by joint decoding

the sensed data can be gathered with low computational cost.A synchronous subspace pursuit algorithm based on joint sparse model and com-pressed sensing theory was proposed.By utilizing the sparsity of the sensed data

it selected the correct joint subspace and reconstruct the original signal group accurately with fewer observations in a backtracking iterative manner.Com-pared with SCoSaMP algorithm and SP algorithm

the proposed algorithm presents better data reconstruction perform-ance under the conditions of different sparsity and sampling rate.

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references

许志强 . 压缩感知 [J ] . 中国科学， 2012 , 42 ( 9 ): 865 - 877 .

XU Z Q . Compressed sensing [J ] . Science China , 2012 , 42 ( 9 ): 865 - 877 .

戴琼海，付长军，季向阳 . 压缩感知研究 [J ] . 计算机学报， 2011 , 34 ( 3 ): 425 - 434 .

DAI Q H , FU C J , JI X Y . Research on compressed sensing [J ] . Chinese Journal of Computers , 2011 , 34 ( 3 ): 425 - 434 .

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

UDDIN B , CELEBI M E , KINGRAVI H . Accurate genomic signal recovery using compressed sensing[C]//International Conference on Pattern Recognition . 2012 : 3144 - 3147 .

KYRIAKIDES I . Adaptive compressive sensing and processing of delay-doppler radar waveforms [J ] . IEEE Transactions on Signal Proc-essing , 2012 , 60 ( 2 ): 730 - 739 .

LIU L , CHONG J S , WANG X Q . Adaptive source location estimation based on compressed sensing in wireless sensor networks [J ] . Interna-tional Journal of Distributed Sensor Networks , 2012 , 8 ( 1 ): 1 - 15 .

LIANG M , LI Y , MENG H . Reconfigurable array design to realize principal component analysis (PCA)-based microwave compressive sensing imaging system [J ] . IEEE Antennas and Wireless Propagation Letters , 2015 , 14 : 1039 - 1042 .

AHN J H . Compressive sensing and recovery for binary images [J ] . IEEE Transactions on Image Processing , 2016 , 25 ( 10 ): 4796 - 4802 .

WANG J , TANG S J , YIN B C . Data gathering in wireless sensor networks through intelligent compressive sensing[C]//International Conference on Computer Communications . 2012 : 603 - 611 .

XU L W , QI X , WANG Y X . Efficient data gathering using com-pressed sparse functions[C]//International Conference on Computer Communications . 2013 : 310 - 314 .

MALLOY M L , NOWAK R D . Near-optimal adaptive compressed sensing [J ] . IEEE Transactions on Information Theory , 2014 , 60 ( 7 ): 4001 - 4012 .

LI X L , TAO X F , LIU Y J . Autoregressive model based data gathering algorithm for wireless sensor networks with compressive sens-ing[C]//International Symposium on Personal,Indoor,and Mobile Ra-dio Communications . 2015 : 2044 - 2048 .

SHEN Y R , HU W , RANA R . Nonuniform compressive sensing for heterogeneous wireless sensor networks [J ] . IEEE Sensors Journal , 2013 , 13 ( 6 ): 2120 - 2128 .

方红，杨海蓉 . 贪婪算法与压缩感知理论 [J ] . 自动化学报， 2011 , 37 ( 12 ): 1414 - 1421 .

FANG H , YANG H R. . Greedy algorithms and compressed sensing [J ] . Acta Automatica Sinica , 2011 , 37 ( 12 ): 1414 - 1421 .

YU Z L , SU J C , YANG F . Fast compressive sensing reconstruction algorithm on FPGA using orthogonal matching pursuit[C]// Interna-tional Symposium on Circuits and Systems . 2016 : 249 - 252 .

LIN Y M , KUO H C , WU A Y . Robust LMS-based compressive sens-ing reconstruction algorithm for noisy wireless sensor net-works[C]//International Conference on Intelligent Green Building and Smart Grid . 2016 : 1 - 5 .

李树涛，魏丹 . 压缩传感综述 [J ] . 自动化学报， 2009 , 35 ( 11 ): 1370 - 1377 .

LI S T , WEI D . A survey on compressive sensing [J ] . Acta Automatica Sinica , 2009 , 35 ( 11 ): 1370 - 1377 .

CANDÈS E J . The restricted isometry property and its implications for compressed sensing [J ] . Comptes Rendus Mathematiqus , 2008 , 346 ( 9 - 10 ): 589 - 592 .

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

BLANCHARD J D , CERMAK M , HANLE D . Greedy algorithms for joint sparse recovery [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 7 ): 1964 - 1704 .

康莉，谢维信，黄建军 . 无线传感器网络中的分布式压缩感知技术 [J ] . 信号处理， 2013 , 29 ( 11 ): 1560 - 1567 .

KANG L , XIE W X , HUANG J J . Distributed compressive sensing for wireless sensor networks [J ] . Journal of Signal Processing , 2013 , 29 ( 11 ): 1560 - 1567 .

GOGNA A , SHUKLA A , AGARWAL H K . Split bregman algorithms for sparse/joint-sparse and low-rank signal recovery:application in compressive hyperspectral imaging[C]//IEEE International Conference on Image Processing . 2014 : 1302 - 1306 .

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