高灵君
上海海事大学信息工程学院 上海 201306
基金项目:国家自然科学基金(61673259)
详细信息
作者简介:张颖：男，1968年生，博士，教授，博士生导师，研究方向为物联网、海事无线通信、无线自组织网络
高灵君：女，1994年生，硕士生，研究方向为物联网信息融合，无线传感网络目标跟踪、预测
通讯作者:张颖 yingzhang@shmtu.edu.cn
中图分类号:TP393计量
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被引次数:0
出版历程
收稿日期:2019-01-28
修回日期:2019-08-29
网络出版日期:2019-09-03
刊出日期:2019-10-01
Target Tracking with Underwater Sensor Networks Based on Grubbs Criterion and Improved Particle Filter Algorithm
Ying ZHANG,,Lingjun GAO
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
Funds:The National Natural Science Foundation of China (61673259)
摘要
摘要:水下无线传感网络(UWSN)执行目标跟踪时，因为各个传感器节点测量值对目标状态估计的贡献不一样以及节点能量有限，所以探索一种好的节点融合权重方法和节点规划机制能够获得更好的跟踪性能。针对上述问题，该文提出一种基于Grubbs准则和互信息熵加权融合的分布式粒子滤波(PF)目标跟踪算法(GMIEW)。首先利用Grubbs准则对传感器节点所获得的信息进行分析检验，去除干扰信息和错误信息。其次，在粒子滤波的重要性权值计算的过程中，引入动态加权因子，采用传感器节点的测量值与目标状态之间的互信息熵，来反映传感器节点提供的目标信息量，从而获得各个节点相应的加权因子。最后，采用3维场景下的簇-树型网络拓扑结构，跟踪监测区域内的目标。实验结果显示，该算法可有效提高水下传感器网络测量数据对目标跟踪预测的准确度，降低跟踪误差。
关键词:水下无线传感器网络/
目标跟踪/
Grubbs准则/
互信息熵/
粒子滤波
Abstract:When the Underwater Wireless Sensor Network (UWSN) performs target tracking, the contributions of the measured values of the nodes are different, and the battery energy carried by the sensor node is limited. Therefore, a good node fusion weight method and node planning mechanism can obtain better tracking performance. A distributed particle filter target tracking algorithm based on Grubbs criterion and Mutual Information Entropy Weighted (GMIEW) fusion is proposed to solve the above problem in this paper. Firstly, the Grubbs criterion is used to analyze and verify the information obtained by the sensor nodes before the information fusion, and the interference information and error information are removed. Secondly, in the process of calculating the importance weight of particle filter, the dynamic weighting factor is introduced. The mutual information entropy between the measured value of the sensor node and the target state is used to reflect the amount of target information provided by the sensor node, so as to obtain the corresponding weighting factor of each node. Finally, the improved cluster-tree network topology is used to track the target in three-dimensional space. Simulation results show that the proposed algorithm improves greatly the accuracy of underwater sensor measurement data for target tracking prediction and reduces the tracking error.
Key words:Underwater Wireless Sensor Network (UWSN)/
Target tracking/
Grubbs criterion/
Mutual information entropy/
Particle Filtering (PF)
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