摘要
在大规模无线传感网络中,针对如何利用合理的信标节点数目去精确定位监测区域内所有未知节点位置信息的问题,利用将已定位的未知节点作为信标节点的通用算法模型,因存在环境影响误差和定位叠加误差,已经不能满足精确定位的要求。提出了带高斯模型的RSSI质心定位算法,利用高斯模型数据处理原则选取高概率发生区的RSSI值,取其几何均值,以排除异常信号传输损耗值;优化已定位未知节点作为信标节点,并对相邻信标节点信号覆盖的重叠区域进行质心平均,进一步减小定位误差。最后通过仿真实验进行比较分析,基于高斯模型的质心定位算法能减少一些小概率、大干扰事件对整体测量定位的影响,增强了定位信息的准确性。
In large-scale wireless sensor network,aiming at how to use a reasonable number of beacon nodes to precisely position all the unknown nodes in the monitoring area,because the general algorithm that using unknown node positioned as beacon node exist environmental impact and superposition of positioning errors,which cannot meet the requirement for precision positioning.Gaussian model with the RSSI centroid localization algorithm is proposed in this paper,by using the gauss model data processing principle of selecting the RSSI values of high probability of occurrence area,take its geometric average,to eliminate abnormal signal transmission loss value.Optimization has to locate the unknown node as a beacon nodes,and the adjacent beacon node coverage overlap area of the center of mass on average,to further reduce the positioning error.Finally,compare and analyze the simulation experiment of centroid localization algorithm based on gauss model can reduce the small probability events,big interference influence on overall measurement positioning,enhance the accuracy of positioning information.
出处
《工业控制计算机》
2021年第6期100-102,106,共4页
Industrial Control Computer
