Multi-group target tracking method based on adaptive predictive clustering in WSN

Shumu LIU; Jian YANG; Yuansong LI

doi:10.11959/j.issn.1000-0801.2016128

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Multi-group target tracking method based on adaptive predictive clustering in WSN

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- Multi-group target tracking method based on adaptive predictive clustering in WSN
- Telecommunications Science Vol. 32, Issue 7, Pages: 68-75(2016)
- 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：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2016128
  CLC： TP393
- Published Online：2016-07，
  
  Published：15 July 2016
- 稿件说明：
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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：

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

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