静态无线网络传感器在设计过程中并未考虑到节点的移动性问题,导致目前的节点定位算法不能满足动态无线传感器网络定位的需求。针对传统动态传感器网络节点定位策略定位精度低、算法复杂、响应时间长等缺点, 在MCL的基础上引入了航位推算法, 提出了一种改进的自适应蒙特卡罗, 将Voronoi图和权值融合在MCL算法的粒子过滤阶段中以提高算法的定位精度。仿真分析说明, 在相同条件下, 与 MCL 算法相比, 改进后的MCL算法平均定位精度提高约50%, 即使采样粒子数目较少时也能保证较高的定位精度。

The design process of static wireless sensor networks does not take into account the node's mobility problems, which leads to the current node location algorithm cannot meet the needs of dynamic positioning in wireless sensor networks. An Improved Adaptive Monte Carlo is proposed, aimed at improving the shortcomings of traditional dynamic sensor network node positioning strategy such as, low accuracy, complex algorithms, slow response, based on the basis of the MCL introduced the dead reckoning. Voronoi map and weights are fused in the particle filter of MCL algorithm to improve the positioning accuracy of the algorithm. Simulation analysis shows under the same conditions,the average localization accuracy is improved byabout 50%. By comparing with the MCL algorithm, even when the number of samples is small, the positioning accuracy can be guaranteed.