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引用本文格式: 张睿敏,陈钟,李晓斌. 多目标量化变分滤波贝叶斯WSN跟踪定位算法 [J]. 四川大学学报: 自然科学版, 2015, 52: 1237~1243.
 
多目标量化变分滤波贝叶斯WSN跟踪定位算法
Multi objective quantization variation filtering based Bayesian prediction algorithm for WSN tracking and positioning
摘要点击 676  全文点击 182  投稿时间:2014-10-15  
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DOI编号   
中文关键词   贝叶斯  多目标优化  差分进化  无线传感器网络  跟踪定位
英文关键词   Bayesian  Multi objective optimization  Differential evolution  Wireless sensor network  Locating and tracking
基金项目   国家自然科学基金(61170263)
作者单位
张睿敏 兰州工业学院软件工程学院 
陈钟 北京大学信息科学技术学院 
李晓斌 兰州工业学院软件工程学院 
中文摘要
    为进一步提高无线传感器网络节点跟踪定位精度降低能耗,提出一种多目标拥挤度差分优化的贝叶斯量化变分滤波预估WSN跟踪定位算法. 首先,针对定位问题,采用贝叶斯量化变分滤波方法对目标下一位置区域进行预测,利用量化变分滤波方式选取合适的定位参与节点,并设计了量化变分滤波的多目标参数优化模型. 其次,针对传统多目标优化算法寻优精度不高的问题,设计了基于种群个体拥挤度状况的多目标差分进化算法,对量化变分滤波算法参数进行优化,实现了滤波参数的多目标优化. 最后,通过实验仿真表明,该算法能够有效实现目标节点的跟踪定位,并可节省能量消耗.
英文摘要
    In order to further improve the wireless sensor network node's tracking accuracy and reduce energy consumption, a multi objective crowding differential quantization variation filtering based Bayesian prediction algorithm for WSN tracking and positioning was proposed. Firstly, for the localization problem, the Bayesian quantization variation filtering method was used to predict the next position of target area, the quantized variation filtering method was used to select the appropriate participating nodes for positioning, for which the multi object parameter optimizing model of quantized variation filtering was designed. Secondly, aiming at the problem of optimization accuracy of traditional multi objective optimization, a multi objective differential evolution algorithm with the method of individual particle crowding distance sorting was proposed for the parameters optimization of quantization variation filtering, which implement the multi objective parameters optimization. Finally, simulation results show that, the algorithm can effectively realize the tracking and locating of the target node, and also reduce the energy consumption.

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