基于自适应采样的移动WSN定位算法

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。

Localization Algorithm of Mobile WSN Based on Adaptive Sampling

Node self-localization plays a critical role in the application of wireless sensor network.The current study of wireless sensor network localization focuses more on the static sensor node localization,while less on localization of mobile wireless sensor network.This paper discusses the mobile wireless sensor network localization based on Sequential Monte Carlo.For the fixed sample number and big computing load of Monte Carlo Localization（MCL）in application,with dynamic setting of the sample number based on the size of Monte Carlo Localization Boxed（MCB）anchor box,a sample-adaptive Monte Carlo Localization Boxed（AMCB）algorithm is proposed.Simulation results indicate that this algorithm could,as compared with the conventional Monte Carlo localization algorithms,reduce the sample number and computing load while ensure the localization accuracy.