无线传感器网络中基于自适应网格的多目标定位算法

王天荆; 李秀琴; 白光伟; 沈航

doi:10.11959/j.issn.1000-436x.2019129

您当前的位置：

首页 >

文章列表页 >

无线传感器网络中基于自适应网格的多目标定位算法

学术通信 | 更新时间：2024-06-05

- 无线传感器网络中基于自适应网格的多目标定位算法
- Multi-target localization algorithm based on adaptive grid in wireless sensor network
- 通信学报 2019年40卷第7期页码：197-207
- 作者机构：
  
  南京工业大学计算机科学与技术学院，江苏南京 211816
- 作者简介：
  
  [ "王天荆（1977- ），女，江苏扬州人，博士，南京工业大学副教授、硕士生导师，主要研究方向为信号处理、无线网络和最优化方法。" ]
  [ "李秀琴（1994- ），女，江苏南京人，南京工业大学硕士生，主要研究方向为无线传感器网络和最优化方法。" ]
  [ "白光伟（1961- ），男，陕西西安人，博士，南京工业大学教授、硕士生导师，主要研究方向为无线网络、数据隐私和网络编码。" ]
  [ "沈航（1984- ），男，江苏南京人，博士，南京工业大学副教授、硕士生导师，主要研究方向为无线网络、数据隐私和网络编码。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61501224);国家自然科学基金资助项目(61502230);国家自然科学基金资助项目(61602235);国家自然科学基金资助项目(61802176);江苏省自然科学基金资助项目(BK20161007);江苏省自然科学基金资助项目(BK20150960);江苏省研究生科研与实践创新计划基金资助项目(SJCX18_0339)
- DOI：10.11959/j.issn.1000-436x.2019129
  中图分类号： TP393
- 网络出版日期：2019-07，
  
  纸质出版日期：2019-07-25
- 稿件说明：
移动端阅览
王天荆, 李秀琴, 白光伟, 等. 无线传感器网络中基于自适应网格的多目标定位算法[J]. 通信学报, 2019,40(7):197-207.

Tianjing WANG, Xiuqin LI, Guangwei BAI, et al. Multi-target localization algorithm based on adaptive grid in wireless sensor network[J]. Journal on communications, 2019, 40(7): 197-207.
王天荆, 李秀琴, 白光伟, 等. 无线传感器网络中基于自适应网格的多目标定位算法[J]. 通信学报, 2019,40(7):197-207. DOI： 10.11959/j.issn.1000-436x.2019129.

Tianjing WANG, Xiuqin LI, Guangwei BAI, et al. Multi-target localization algorithm based on adaptive grid in wireless sensor network[J]. Journal on communications, 2019, 40(7): 197-207. DOI： 10.11959/j.issn.1000-436x.2019129.

摘要

针对无线传感器网络中基于RSS的多目标定位具有天然稀疏性的问题，提出了基于自适应网格的多目标定位算法，将多目标定位问题分解为大尺度网格定位和自适应网格定位2个阶段。大尺度网格定位阶段根据序贯压缩感知原理确定最优观测次数，再利用lp (0＆lt; p＆lt;1)最优化重构出存在目标的初始网格；自适应网格定位阶段根据压缩感知原理自适应划分初始网格，再利用lp最优化重构出目标的精确位置。仿真结果表明，相较于传统的基于压缩感知的多目标定位算法，所提算法在目标个数未知的场景下具有更高的定位精度和更低的定位时延，且更适合大规模无线传感器网络的多目标定位问题。

Abstract

The RSS-based multi-target localization has the natural property of the sparsity in wireless sensor networks.A multi-target localization algorithm based on adaptive grid in wireless sensor networks was proposed

which divided the multi-target localization problem into two phases:large-scale grid-based localization and adaptive grid-based localization.In the large-scale grid-based localization phase

the optimal number of measurements was determined due to the sequential compressed sensing theory

and then the locations of the initial candidate grids were reconstructed by applying lp (0＆lt; p＆lt;1) optimization.In the adaptive grid-based localization phase

the initial candidate grids were adaptively partitioned according to the compressed sensing theory

and then the locations of the targets were precisely estimated by applying lpoptimization once again.Compared with the traditional multi-target localization algorithm based on compressed sensing

the simulation results show that the proposed algorithm has higher localization accuracy and lower localization delay without foreknowing the number of targets.Therefore

it is more appropriate for the multi-target localization problem in the large-scale wireless sensor networks.

关键词

Keywords

references

PANWAR A , KUMAR S A . Localization schemes in wireless sensor networks [C ] // International Conference on Advanced Computing ＆Communication Technologies . IEEE , 2012 : 1 - 5 .

ZHOU Y M , LI L Y . A trust-aware and location-based secure routing protocol for WSN [J ] . Applied Mechanics ＆ Materials , 2013 , 373-375 : 1931 - 1934 .

BISWAS J , VELOSO M . Depth camera based indoor mobile robot localization and navigation [C ] // IEEE International Conference on Robotics and Automation . IEEE , 2011 : 1697 - 1702 .

FAWZY A E , AMER A , SHOKAIR M , et al . Four-layer routing protocol with location based topology control of active nodes in WSN [C ] // International Conference on Computer Engineering ＆ Systems . IEEE , 2017 .

LEE D K , KIM T H , JEONG S Y , et al . A three-tier middleware architecture supporting bidirectional location tracking of numerous mobile nodes under legacy WSN environment [J ] . Journal of Systems Architecture , 2011 , 57 ( 8 ): 735 - 748 .

CACERES M A , SOTTILE F , GARELLO R , et al . Hybrid GNSS-TOA localization and tracking via cooperative unscented Kalman filter [C ] // International Symposium on Personal,Indoor and Mobile Radio Communications Workshops . IEEE Xplore , 2010 : 272 - 276 .

HUANG B , XIE L , YANG Z . TDOA-based source localization with distance-dependent noises [J ] . IEEE Transactions on Wireless Communications , 2015 , 14 ( 1 ): 468 - 480 .

KAUR A , KUMAR P , GUPTA G P . A weighted centroid localization algorithm for randomly deployed wireless sensor networks [J ] . Journal of King Saud University-Computer and Information Sciences , 2019 , 31 ( 1 ): 82 - 91 .

CHENG H Q , WANG H K , WANG H . Research on centroid localization algorithm that uses modified weight in WSN [C ] // International Conference on Network Computing ＆ Information Security . IEEE , 2011 : 287 - 291 .

李华亮 , 钱志鸿 , 田洪亮 . 基于核函数特征提取的室内定位算法研究 [J ] . 通信学报 , 2017 , 38 ( 1 ): 158 - 167 .

LI H L , QIAN Z H , TIAN H L . Research on indoor localization algorithm based on kernel principal component analysis [J ] . Journal on Communications , 2017 , 38 ( 1 ): 158 - 167 .

AHMADI H , POLO A , MORIYAMA T , et al . Semantic wireless localization of Wi-Fi terminals in smart buildings [J ] . Radio Science , 2016 , 51 ( 6 ): 876 - 892 .

TSAIG Y , DONOHO D L . Extensions of compressed sensing [J ] . Signal Processing , 2006 , 86 ( 3 ): 549 - 571 .

FENG C , VALAEE S , TAN Z . Multiple target localization using compressive sensing [C ] // Global Telecommunications Conference . IEEE , 2010 : 1 - 6 .

刘磊 , 张建军 , 陆阳 , 等 . 仅依赖连通度的压缩感知多目标定位方法 [J ] . 通信学报 , 2016 , 37 ( 5 ): 152 - 164 .

LIU L , ZHANG J J , LU Y , et al . Multiple targets localization via compressive sensing from mere connectivity [J ] . Journal on Communications , 2016 , 37 ( 5 ): 152 - 164 .

CUI B , ZHAO C , FENG C , et al . An improved greedy matching pursuit algorithm for multiple target localization [C ] // International Conference on Instrumentation . IEEE , 2013 : 926 - 930 .

何风行 , 余志军 , 吕政 , 等 . 基于 RSS 的 WSN 多目标定位压缩感知算法优化 [J ] . 南京邮电大学学报(自然科学版) , 2012 , 32 ( 1 ): 24 - 28 .

HE F H , YU Z J , LYU Z , et al . The optimization of compressed sensing algorithm for multi-target localization via RSS in WSN [J ] . Journal of Nanjing University of Posts and Telecommunications (Natural Science) , 2012 , 32 ( 1 ): 24 - 28 .

游康勇 , 杨立山 , 刘玥良 , 等 . 基于稀疏贝叶斯学习的网格自适应多源定位 [J ] . 电子与信息学报 , 2018 , 40 ( 9 ): 2150 - 2157 .

YOU K Y , YANG L S , LIU Y L , et al . Adaptive grid multiple sources localization based on sparse Bayesian learning [J ] . Journal of Electronics ＆ Information Technology , 2018 , 40 ( 9 ): 2150 - 2157 .

YAN J , YU K , CHEN R , et al . An improved compressive sensing and received signal strength-based target localization algorithm with unknown target population for wireless local area networks [J ] . Sensors , 2017 , 17 ( 6 ): 1246 - 1264 .

MO Q , SHEN Y . A remark on the restricted isometry property in orthogonal matching pursuit [J ] . IEEE Transactions on Information Theory , 2012 , 58 ( 6 ): 3654 - 3656 .

RUI W , WEI H , CHEN D R . The exact support recovery of sparse signals with noise via orthogonal matching pursuit [J ] . IEEE Signal Processing Letters , 2013 , 20 ( 4 ): 403 - 406 .

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

BERG E V D , FRIEDLANDER M P . Sparse optimization with least-squares constraints [J ] . SIAM Journal on Optimization , 2010 , 21 ( 4 ): 1201 - 1229 .

LAN K C , WEI M Z . A compressibility-based clustering algorithm for hierarchical compressive data gathering [J ] . IEEE Sensors Journal , 2017 , 17 ( 8 ): 2550 - 2562 .

SONG C B , XIA S T , LIU X J . Improved analysis for subspace pursuitalgorithm in terms of restricted isometry constant [J ] . IEEE Signal Processing Letters , 2014 , 21 ( 11 ): 1365 - 1369 .

CHEN Q , HUA L , MIN Y , et al . RSSI ranging model and 3D indoor positioning with ZigBee network [C ] // IEEE International Conference on Position Location ＆ Navigation Symposium . IEEE , 2012 : 23 - 26 .

RAO B D , DELGADO K K . An affine scaling methodology for best basis selection [J ] . IEEE Transactions on Signal Processing , 1999 , 47 ( 1 ): 187 - 200 .

WEI Y , LI W , CHEN T . Node localization algorithm for wireless sensor networks using compressive sensing theory [J ] . Personal and Ubiquitous Computing , 2016 , 20 ( 5 ): 809 - 819 .

浏览量

856

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向WSN异常节点检测的融合重构机制与对比学习方法

基于半固定分簇的无线传感器网络节能分簇路由算法

基于分组序列码本和压缩感知的大规模无源多址方案

TDoA定位盲区分析与节点部署策略研究

基于基扩展模型的改进正则化正交匹配追踪V2X快时变SC-FDMA信道估计