定位技术是无线传感器网络中关键的基础支撑技术, 目前提出了许多静态网络的节点定位算

法, 移动无线传感器网络的定位研究相对较少. 针对定位节点和参考节点随机运动的网络模型, 提出了

一个基于动态网格划分的蒙特卡罗定位算法. 算法中当接收的参考节点数超过一定阈值时使用最远距

离节点选择模型, 选出部分参考节点参与定位和信息转发, 节约能耗. 接着基于选择的或所有接收的参

考节点构建采样区域, 进行网格划分, 使用网格单元数计算最大采样次数, 在采样区域内采样并使用误

差补偿的运动模型进行过滤, 提高了采样效率, 减少了计算开销, 并保证了较好的定位精度. 仿真实验表

明算法在定位精度, 计算开销、能耗等方面都具有较好的性能.-Positioning technology is critical in wireless sensor networks based supporting technologies, currently made many static network node localization algorithms, the positioning of mobile wireless sensor network research is relatively small. Locate node and a reference node for random motion network model, proposed a Dynamic meshing based Monte Carlo localization algorithm. algorithm when the received reference nodes exceeds a certain threshold, the most distant node selection model selects some reference nodes involved locating and forwarding information, save energy. then based on the selection or building all received reference node sampling area, meshing, the number of grid cells used to calculate the maximum sampling frequency, the sampling area sampling and using motion model error compensation filter to improve sampling efficiency, reducing the computational overhead and to ensure a better positioning accuracy. Simulation experiments show that the algorithm in positioning accuracy,