基于粒子群的DV_Hop算法优化

原DV_Hop算法中存在节点间距离估算的累计误差以及待测节点坐标求解时的误差问题。在平均跳距的计算阶段,信标节点先后以两个通信半径广播自身位置信息,精确了节点间最小跳数值,加入修正因子来校正平均跳距,得到更精确的未知节点坐标。采用基于线性优化惯性权重和线性加权改进的学习因子同步变化的粒子群算法来优化待测节点位置解析误差,降低待测节点的平均定位误差。仿真结果表明,与原有算法相比,该方法可以有效地降低估算距离误差,提高待测节点的定位精度。

OPTIMIZATION OF DV_HOP ALGORITHM BASED ON PARTICLE SWARM OPTIMIZATION

This paper deals with the accumulative error of distance estimation between nodes and the error problem when coordinate of nodes to be measured in the original DV_Hop algorithm.In the calculation stage of average hop distance,beacon node broadcasted its own position information with two communication radii successively,which accurately corrected the minimum hop value between nodes.Next,a correction factor was added to correct the average hop distance,so as to obtain more accurate coordinates of unknown nodes.Then,particle swarm optimization(PSO),which was based on linear optimization inertia weight and linear weighting improved learning factor synchronization,was adopted to optimize the position error of the nodes to be measured and reduce the average positioning error of the nodes to be measured.The simulation results show that compared with the original algorithms,our algorithm can effectively reduce the estimated distance error and improve the positioning accuracy of the nodes to be measured.