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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。
In order to improve the accuracy of WSNs moving target location tracking with reduced resource consumption and bandwidth, a Bayesian moving target location tracking algorithm based on Kullback-Leibler bifurcation variational filtering is proposed. Firstly, the Bayesian state evolution model of WSNs mobile positioning is constructed by using Gaussian and Wishart distribution without considering the restriction of velocity and directional movement. Secondly, the observation model of moving target positioning is constructed based on the path loss model. Secondly, by using Kullback-Leibler Disagreement to construct the error calculation model of the variational filtering, and realize the position estimation of the mobile node by activating the surrounding nodes. The two processes of the integrated prediction and updating of the recursive probability calculation process are designed, and the synchronization of the positioning and the target tracking is achieved. Finally, The simulation verifies the advantages of the proposed model in tracking accuracy and resource saving.