In order to achieve high tracking precision of moving target positioning in wireless sensor networks (WSNs) with low energy consumption and bandwidth consumption,we propose a WSNs Bayesian localization and tracking algorithm based on Kullback-Leibler divergence filtering.Firstly,we use the Gaussian and Wishart distribution without considering the speed limits and restrictions of movement direction to construct a mobile localization Bayesian state evolution model for WSNs,as well as a moving target positioning observation model based on the path loss model.Secondly,we use the Kullback Leibler to construct a divergence filtering error calculation model,which can estimate the position of the goal of the mobile node through activating the surrounding nodes.The recursive probability calculation process we designed integrates prediction and updates process,and realizes synchronous target localization and tracking.Simulation results show that the proposed model has advantages in tracking accuracy and resource saving.%为了在降低资源能耗和带宽占用情况下,提高无线传感器网络WSNs移动目标定位跟踪的精度,提出了基于Kullback-Leibler分歧的变分滤波的WSNs贝叶斯移动目标定位跟踪算法.首先,利用高斯和Wishart分布在不考虑速度限制和方向移动限制情况下,构建WSNs移动定位的贝叶斯状态演化模型,并基于路径损耗模型构建移动目标定位的观测模型;其次,利用Kullback-Leibler分歧构建变分滤波的误差计算模型,通过周围激活节点实现移动节点目标的位置估计,设计了递归概率计算过程综合预测和更新两个过程,并实现了定位和目标跟踪的同步化;最后,通过仿真验证了所提模型在跟踪精度和资源节约上的优势.
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