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节点自定位是无线传感器网络的关键技术之一。当前对无线传感器网络定位的研究主要集中静态节点定位,移动无线传感器网络定位研究相对较少。研究了基于序列蒙特卡罗方法的移动无线传感器网络定位。针对蒙特卡罗定位采用固定样本数,计算量大的缺点,根据蒙特卡罗定位盒(MCB)算法的锚盒子大小动态设置样本数,提出一种自适应采样蒙特卡罗盒定位算法。仿真表明,该算法在保持定位精度的同时有效地减小了采样次数,节约了计算量。
Node self-localization is one of the key technologies of wireless sensor network. At present, the research on the localization of wireless sensor networks mainly focuses on the location of static nodes. There are relatively few studies on the localization of mobile wireless sensor networks. The location of mobile wireless sensor network based on sequence Monte Carlo method is studied. Aiming at the shortcomings of Monte Carlo positioning, such as fixed sample size and large amount of calculation, the number of samples is dynamically set according to the anchor box size of the Monte Carlo positioning box (MCB) algorithm. An adaptive sampling Monte Carlo box positioning algorithm is proposed. The simulation shows that the algorithm can effectively reduce the number of sampling and save the computation while maintaining the positioning accuracy.