一种适用于移动传感器网络的增强型蒙特卡罗定位跟踪算法

Enhanced Monte Carlo Localization and Tracking Algorithm for Mobile Wireless Sensor Network

该文提出一种在低锚节点密度的移动传感器网络中实现定位跟踪的方法。利用受控的洪泛方式提高锚节点利用效率,采用遗传交叉操作加快预测阶段的抽样,采用插值方法对节点运动速度及方向进行预测,利用位置估计精度优于自身的1跳邻居节点的信息强化滤波条件。仿真实验结果表明,该文算法与传统算法相比加快了收敛速度,提高了定位精度,改善了在低锚节点密度时的性能。

A localization and tracking algorithm suitable for mobile wireless sensor network is proposed. The algorithm uses controlled flood method to improve the using efficiency of the anchor nodes,uses cross operation to accelerate the sampling process and interpolation operation to predict the velocity and angle. An estimate precision function is also proposed so that one node could make the full use of all of the outstanding neighbor nodes＇ information. Simulation results show that the algorithm outperforms the traditional algorithm in the convergence speed,localization accuracy and the requirement of anchor density.