基于离散萤火虫压缩感知重构的无线传感器网络多目标定位

刘洲洲; 王福豹

doi:10.3788/OPE.20142207.1904

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基于离散萤火虫压缩感知重构的无线传感器网络多目标定位

信息科学 | 更新时间：2020-08-13

- 基于离散萤火虫压缩感知重构的无线传感器网络多目标定位
- Multiple target localization in WSNs via CS reconstruction based on discrete GSO algorithm
- 光学精密工程 2014年22卷第7期页码：1904-1911
- 作者机构：
  
  1. 西北工业大学电子信息学院,陕西西安,710072
  2. 西安航空学院,陕西西安,710077
- 作者简介：
  
  [ "刘洲洲（1981－），男，山西运城人，博士研究生，讲师，2004年、2007年于西北工业大学分别获得学士、硕士学位，主要从事无线传感器网络方面的研究。E-mail：liuzhouzhou8192@126.com" ]
  [ "王福豹（1963－），男，山西运城人，博士，教授，博士生导师，主要从事计算机网络、网络与信息安全、无线通信网络、数字多媒体通信等方面的研究。E-mail：wfb1963@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目（No.61103242）()
- DOI：10.3788/OPE.20142207.1904
  中图分类号： TP393
- 收稿日期：2013-09-16，
  
  修回日期：2013-10-29，
  
  纸质出版日期：2014-07-25
- 稿件说明：
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刘洲洲, 王福豹,. 基于离散萤火虫压缩感知重构的无线传感器网络多目标定位[J]. 光学精密工程, 2014,22(7): 1904-1911

LIU Zhou-zhou, WANG Fu-bao,. Multiple target localization in WSNs via CS reconstruction based on discrete GSO algorithm[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1904-1911
刘洲洲, 王福豹,. 基于离散萤火虫压缩感知重构的无线传感器网络多目标定位[J]. 光学精密工程, 2014,22(7): 1904-1911 DOI： 10.3788/OPE.20142207.1904.

LIU Zhou-zhou, WANG Fu-bao,. Multiple target localization in WSNs via CS reconstruction based on discrete GSO algorithm[J]. Editorial Office of Optics and Precision Engineering, 2014,22(7): 1904-1911 DOI： 10.3788/OPE.20142207.1904.

摘要

研究了压缩感知（CS）理论在无线传感器网络（WSNs）多目标定位中的应用。提出一种基于离散萤火虫算法的压缩感知重构方法，并设计了具体算法实现流程，该算法摆脱了传统压缩感知重构算法对稀疏度

的依赖且能够准确地重构出原始信号。基于此，将新的压缩感知重构算法应用于WSNs目标定位，建立了WSNs系统模型，构造了合理的测量矩阵和稀疏矩阵，并分析了测量矩阵与重构结果之间的关系，最终实现了WSNs多目标定位。仿真结果表明该方法在稀疏信号重构性能及多目标定位精度方面具有较好效果，定位精度优于贪婪匹配跟踪（GMP）算法、正交匹配追踪（OMP）算法和最大似然估计（MLE）算法，且用于WSNs定位的传感器节点数目减少了20%，抗噪性达到了20 dB。

Abstract

The application of Compressed Sensing(CS)theory to multiple target localization in Wireless Sensor Networks(WSNs)was explored.A CS reconstruction method based on Discrete Glowworm Swarm Optimization(DGSO) algorithm was proposed and the algorithm processing was designed.Different from the traditional reconstruction method

the DGSO algorithm is independent on the sparse

and can accurately reconstruct the original signal.The improved CS was applied to the multiple target localization in WSNs

and the WSNs application model was established.Then

a reasonable measuring matrix and a sparse matrix were constructed

and the relationship between measuring matrix and reconstructed results was analyzed.Finally

the multiple target localization was achieved in WSNs.The simulation results show that this method has better effect in the sparse signal reconstruction and multi target locating precision

and its location precision is better than those of Greedy Matching Pursuit(GMP)

Orthogonal Matching Pursuit(OMP) and mum Likelihood Estimation(MLE).Moreover

it reduces the network communication data amounts

extends the lifetime of WSNs.The number of sensor nodes for localization of WSNs is reduced by 20%

and the anti-noise can reach to 20 dB.

关键词

Keywords

references

张晓平,刘桂雄,周松斌. 利用最小二乘支持向量机实现无线传感器网络的目标定位[J]. 光学精密工程,2010,18(9):2060-2068. ZHANG X P,LIU G X,ZHOU S B. Target localization based on LSSVR in wireless sensor networks[J]. Opt. Precision Eng. ,2010,18(9): 2060-2068. (in Chinese)

朱剑,赵海,徐久强,等. 无线传感器网络中的定位模型[J]. 软件学报,2011,22(7):1612-1625. ZHU J,ZHAO H,XU J Q,et al. Localization model in wireless sensor networks[J]. Journal of Software,2011,22(7):1612-1625. (in Chinese)

田丰,郭巍,王传云,等. 大规模WSNs多层定位模型与误差分析[J]. 电子学报,2009,37(5):1095-1099. TIAN F,GUO W,WANG CH Y,et al. Multilevel localization model and localization error analyze for large scale WSNs[J]. Acta Electronica Sinica,2009,37(5):1095-1099. (in Chinese)

王福豹,史龙,任丰原. 无线传感器网络中的自身定位系统和算法[J]. 软件学报,2005,16(5):857-868. WANG F B,SHI L,REN F Y. Self-localization systems and algorithms for wireless sensor networks[J]. Journal of Software,2005,16(5):857-868. (in Chinese)

AKSU H,AKSOY D,KURPEOGLU I. A study of localization metrics: Evaluation of position errors in wireless sensor networks[J]. Computer Networks,2011,55(15): 3562-3577.

何风行,余志军,刘海涛. 基于压缩感知的无线传感器网络多目标定位算法[J]. 电子与信息学报,2012,34(3):716-721. HE F H,YU ZH J,LIU H T. Multiple target localization via compressed sensing in wireless sensor networks[J]. Journal of Electronics & Information Technology,2012,34(3):716-721. (in Chinese)

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

焦李成,杨淑媛,刘芳,等. 压缩感知回顾与展望[J]. 电子学报,2011,39(7):1651-1662. JIAO L CH,YANG SH Y,LIU F,et al. Development and prospect of compressive sensing[J]. Acta Electronica Sinica,2011,39(7):1651-1662. (in Chinese)

DONOHO D L. For most large underdetermined systems of linear equations,the minimal l1 norm solution is also the sparsest solution[J]. Communications on Pure and Applied Mathematics,2006,59(6): 797-829.

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

NEEDELL D,TROPP J A. CoSaMP: Iterative signal recovery from incomplete and inaccurate samples[J]. ACM Technical Report 200801,California Institute of Technology,Pasadena,July 2008.

张宗念,黄仁泰,闫敬文. 压缩感知信号盲稀疏度重构算法[J]. 电子学报,2011,39(1): 18-22. ZHANG Z N,HUANG R T,YAN J W. A blind sparsity reconstruction algorithm for compressed sensing signal[J]. Acta Electronica Sinica,2011,39(1): 18-22. (in Chinese)

CEVHER V,DUARTE M F,BARANIUK R G. Distributed target localization via spatial sparsity. Proceedings of the European Signal Processing Conference,Lausanne,Switzerland,Aug,2008: 25-29.

FENG CHEN,VALAEE S,TAN ZH H. Multiple target localization using compressive sensing. GLOBECOM 2009,Piscataway: IEEE,2009:1-6.

WU ZHUO,YANG H. Power allocation of cooperative amplify-and-forward communications with multiple relays[J]. The Journal of China Universities of Posts and Telecommunications,2011,8(4): 65-69.

石光明,刘丹华,高大化,等. 压缩感知理论及其研究进展[J]. 电子学报,2009,37(5):1070-1081. SHI G M,LIU D H,GAO D H,et al. Advances in theory and application of compressed sensing[J]. Acta Electronica Sinica,2009,37(5): 1070-1081. (in Chinese)

KRISHNANAND K N,GHOSE D. Theoretical foundations for rendezvous of glowworm-inspired agent swarms at multiple locations[J]. Robotics and Autonomous Systems,2008,56(7): 549-569.

周永权,黄正新,刘洪霞. 求解TSP问题的离散型萤火虫群优化算法[J]. 电子学报,2012,40(6):1164-1170. ZHOU Y Q,HUANG ZH X,LIU H X. Discrete Glowworm Swarm Optimization Algorithm for TSP problem[J]. Acta Electronica Sinica,2012,40(6):1164-1170. (in Chinese)

吴新杰,黄国兴,王静文. 压缩感知在电容层析成像流型辨识中的应用[J]. 光学精密工程,2013,21(4): 1062-1068. WU X J,HUANG G X,WANG J W. Application of compressed sensing to flow pattern identification of ECT[J]. Opt. Precision Eng. ,2013,21 (4): 1062-1068. (in Chinese)

朱秋平,颜佳,张虎,等. 基于压缩感知的多特征实时跟踪[J]. 光学精密工程,2013,21(2): 437-444. ZHU Q P,YAN J,ZHANG H,et al. Real-time tracking using multiple features based on compressive sensing[J]. Opt. Precision Eng. ,2013,21 (2): 437-444. (in Chinese)

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