WSN中基于自适应预测聚类的多组群目标的跟踪方法

刘述木; 杨建; 黎远松

doi:10.11959/j.issn.1000-0801.2016128

您当前的位置：

首页 >

文章列表页 >

WSN中基于自适应预测聚类的多组群目标的跟踪方法

研究与开发 | 更新时间：2024-06-05

- WSN中基于自适应预测聚类的多组群目标的跟踪方法
- Multi-group target tracking method based on adaptive predictive clustering in WSN
- 电信科学 2016年32卷第7期页码：68-75
- 作者机构：
  
  1. 四川工程职业技术学院，四川德阳618000
  2. 四川理工学院，四川自贡643000
- 作者简介：
  
  [ "刘述木（1978-），男，四川工程职业技术学院工程师，主要研究方向为 WSN、智能算法等。" ]
  [ "杨建（1979-），男，四川工程职业技术学院工程师，主要研究方向为 WSN、计算机应用等。" ]
  [ "黎远松（1970-），男，四川理工学院副教授，主要研究方向为WSN、物联网等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016128
  中图分类号： TP393
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-15
- 稿件说明：
移动端阅览
刘述木, 杨建, 黎远松. WSN中基于自适应预测聚类的多组群目标的跟踪方法[J]. 电信科学, 2016,32(7):68-75.

Shumu LIU, Jian YANG, Yuansong LI. Multi-group target tracking method based on adaptive predictive clustering in WSN[J]. Telecommunications science, 2016, 32(7): 68-75.
刘述木, 杨建, 黎远松. WSN中基于自适应预测聚类的多组群目标的跟踪方法[J]. 电信科学, 2016,32(7):68-75. DOI： 10.11959/j.issn.1000-0801.2016128.

Shumu LIU, Jian YANG, Yuansong LI. Multi-group target tracking method based on adaptive predictive clustering in WSN[J]. Telecommunications science, 2016, 32(7): 68-75. DOI： 10.11959/j.issn.1000-0801.2016128.

摘要

针对多传感器目标跟踪中的能源使用和跟踪精度之间的不平衡问题，提出了一种权衡网络寿命和精度的方法，即基于自适应预测聚类的多组群目标跟踪方法（APCMT），实现了同时跟踪多个组群。首先进行聚类，即捕捉组群行为属性的改变，例如形成、合并以及分裂；然后选择传感器，激活对组群区域有贡献的传感器，并进行组群跟踪。仿真场景在1 000 m×1 000 m的正方形区域内，随机部署500个传感器，与Kalman、效能节点选择（EENS）方法以及改进的动态簇（IDC）方法相比，提出的方法在跟踪精度方面更高。由于需要激活的传感器更少、计算时间更短，网络寿命得到了明显的提升。

Abstract

Aiming at the imbalance between energy using and tracking accuracy in multi-sensor target tracking

a method of trade-off network lifetime and accuracy was proposed.It was a multi-group target tracking method based on adaptive predictive clustering (APCMT)

realizing the simultaneous tracking of multiple groups.Firstly

cluster was realized

which was to capture the changes of group behavior attributes

such as forming

mergering and spliting.Then

sensors were selected

which were expected to contribute to the group area sensor activation

and the sensors were used for group tracking.Scene simulation was in a square area of 1 000 m ×1 000 m with 500 randomly deployed sensors.The effectiveness of the proposed method was verified by the simulation results.Compared with Kalman

energy-effective node selection (EENS) method and the improved dynamic cluster (IDC) method

the tracking precision of proposed method was higher.And because of the number of activate sensors was less

the computational time was less

the network lifetime had been improved significantly.

关键词

Keywords

references

张希伟，戴海鹏，徐力杰，等 . 无线传感器网络中移动协助的数据收集策略 [J ] . 软件学报， 2013 , 37 ( 2 ): 198 - 214 .

ZHANG X W , DAI H P , XU L J , et al . Mobility-assisted data gathering strategies in WSNs [J ] . Journal of Mobile Communications , 2013 , 37 ( 2 ): 198 - 214 .

赵建军，王怀宇，赵泽阳，等 . WSN中基于多分辨率和压缩感知的数据融合方案 [J ] . 电信科学， 2014 , 30 ( 9 ): 92 - 99 .

ZHAO J J , WANG H Y , ZHAO Z Y , et al . Data aggregation scheme based on multi-resolution and compressive sensing in wireless sensor network [J ] . Telecommunications Science , 2014 , 30 ( 9 ): 92 - 99 .

CAO D , JIN B , DAS S K , et al . On collaborative tracking of a target group using binary proximity sensors [J ] . Journal of Parallel ＆ Distributed Computing , 2010 , 70 ( 8 ): 825 - 838 .

夏候凯顺，严娟，叶小朋，等 . 基于Kalman滤波的无线传感器网络多目标跟踪 [J ] . 中山大学学报 ( 自然科学版 ), 2014 , 32 ( 2 ): 18 - 22 .

XIAHOU K S , YAN J , YE X P , et al . Multi-target tracking based on kalman filter and WSN [J ] . Acta Scientiarum Naturalium Universitatis Sunyatseni , 2014 , 32 ( 2 ): 18 - 22 .

GNING A , MIHAYLOVA L , MASKELL S , et al . Group object structure and state estimation with evolving networks and Monte Carlo methods [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 4 ): 1383 - 1396 .

张明波，陆锋，申排伟，等 . R树家族的演变和发展 [J ] . 计算机学报 , 2005 , 28 ( 3 ): 289 - 300 .

ZHANG M B , LU F , SHEN P W , et al . The evolvement and progress of R-tree family [J ] . Chinese Journal of Computers , 2005 , 28 ( 3 ): 289 - 300 .

AVCI B , TRAJCEVSKI G , SCHEUERMANN P . Managing evolving shapes in sensor networks [C ] // The 26th International Conference on Scientific and Statistical Database Management , June 30 - July 2 , 2014 , Aalborg,Denmark . New York : ACM Press , 2014 : 1 - 12 .

CORPORATION H P . An energy-efficient node selection algorithm in bearings-only target tracking sensor networks [J ] . International Journal of Distributed Sensor Networks , 2014 , 41 ( 1 ): 118 - 121 .

崔亚峰 . 无线传感器网络目标跟踪算法的研究 [D ] ．太原：太原理工大学 , 2015 .

CUI Y F . Research target tracking algorithm of wireless sensor networks [D ] . Taiyuan: Taiyuan University of Technology , 2015 .

ARMAGHANI F R , GONDAL I , KAMRUZZAMAN J , et al . Dynamic clusters graph for detecting moving targets using WSNs [C ] // IEEE 38th Vehicular Technology Conference , Sept 3 - 6 , 2012 , Quebec City,QC,Canada . New Jersey : IEEE Press , 2012 : 1 - 5 .

徐翠 . 复杂网络中基于数据场的自适应聚类算法研究 [D ] ．武汉：华中师范大学 , 2014 .

XU C . Research on adaptive clustering algorithm based on data field in complex network [D ] . Wuhan: Central China Normal University , 2014 .

张军强，王汝传，黄海平 . 基于分簇的无线多媒体传感器网络数据聚合方案研究 [J ] . 电子与信息学报， 2014 , 35 ( 1 ): 8 - 14 .

ZHANG J Q , WANG R C , HUANG H P . Research on cluster-based data aggregation for wireless multimedia sensor networks [J ] . Journal of Electronics ＆ Information Technology , 2014 , 35 ( 1 ): 8 - 14 .

ZHAO W , HAN Y , WU H , et al . Weighted distance based sensor selection for target tracking in wireless sensor networks [J ] . IEEE Signal Processing Letters , 2009 , 16 ( 8 ): 647 - 650 .

张万里，何金刚，赵红梅 . 改进的Rao-Blackwellized粒子滤波算法在目标跟踪中的应用 [J ] . 四川兵工学报， 2014 , 36 ( 7 ): 82 - 86 .

ZHANG W L , HE J G , ZHAO H M . Target track based on improved Rao-Blackwellized particle filter algorithm [J ] . Journal of Sichuan Ordnance , 2014 , 36 ( 7 ): 82 - 86 .

杨小军 . 基于性能边界和量化数据的WSN目标跟踪传感器选择算法 [J ] . 电子学报 , 2014 , 42 ( 6 ): 1081 - 1085 .

YANG X J . Sensor selection for target tracking in wireless sensor networks based on performance bounds and quantized data [J ] . Acta Electronica Sinica , 2014 , 42 ( 6 ): 1081 - 1085 .

浏览量

468

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于服务质量的层次化结构资源分配算法

基于IPSO-MC融合算法的无线传感器节点定位

面向铁路环境监测的无线传感器网络通信性能分析

基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法

基于博弈论的无线传感器网络恶意程序传播模型