WSN中基于自适应预测聚类的多组群目标的跟踪方法

doi:10.11959/j.issn.1000-0801.2016128

摘要/Abstract

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。

关键词: 无线传感器网络, 目标跟踪, 多传感器, 预测聚类, 多组群, 跟踪精度

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.

Key words: wireless sensor network, target tracking, multi-sensor, predictive clustering, multi-group, tracking accuracy

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.

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估算参数和能量参数及其设置值"

类别	参数	设定值
估算	粒子滤波样品的数量np	100
参数	过程噪声Q	0.1、0.5
	测量噪声驻兹	0.01
能量	无线电电路能量E_elec/nJ	50
参数	多路径模型的放大器能量 E_mp/(pJ·(bit·m⁴)^-1)	0.001 3
	自由空间模型的放大器能量 $E_{f_{s}} / (p J \cdot (^{b i t \cdot m^{2}) - 1})$	10
	数据报文l_{for one group}/byte	400

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