基于跳距与改进粒子群算法的DV-Hop定位算法  

DV-Hop Localization Algorithm Based on Hop-Size and Improvement Particle Swarm Optimization

作　　者：范时平[1] 罗丹[1] 刘艳林[1] 

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065

出　　处：《传感技术学报》2016年第9期1410-1415,共6页Chinese Journal of Sensors and Actuators

基　　金：基于物联网技术的呼吸;脉搏异变及跌落的实时监测与报警的关键技术研究项目(61171190)

摘　　要：针对DV-Hop定位算法定位精度不高的问题,提出一种带改进的权重平均每跳距离与改进的粒子群算法以改进经典DV-Hop算法。一方面,提出跳距误差与估计距离误差的加权平均值,修正原始的平均每跳距离。另一方面,采用分段的指数、对数递减权重改进粒子群的权重;同时,结合人工鱼群位置更新的优点来改进粒子群算法的位置更新。用改进的粒子群算法求解未知节点坐标,以提高定位精度。实验仿真表明,该算法的定位精度和稳定性与其他算法相比有明显的改善。In order to solve DV- Hop low localization accuracy,a novel localization method based on modifiedweighted average hop- size and improved particle swarm optimization algorithm is proposed. On the one hand,weighted average both hop-size error and estimated distance error modify initial average hop-size. On the otherhand,index and logarithmic decrement of piecewise function improve inertia weight of PSO. Furthermore,combin-ing with localization update of Atificial Fish Swarm Algorithm improve PSO＇s localization update. Then,improvedalgorithm estimate unknown node coordination. The experiment shows localization accuracy and stability of themethod is greatly improved.

关 键 词：无线传感器网络 DISTANCE Vector-Hop Algorithm 改进的粒子群算法 平均每跳距离 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]