摘要
针对DV-Hop定位算法利用跳数乘以平均跳距来估算距离并采用极大似然估计法定位而导致误差较大的问题,提出一种最优跳距和改进粒子群的DV-Hop算法即OPDV-Hop。该算法利用节点的通信半径对锚节点间跳数进行修正;根据未知节点邻近区域的平均跳距来优化当前跳距;用改进的粒子群算法来优化未知节点坐标。仿真结果表明,相比DV-Hop算法、基于粒子群的DV-Hop算法以及基于改进粒子群的定位算法,OPDVHop算法的定位误差分别减小了18%、13%和7%左右,它能够有效地降低估算距离误差,提高定位精度。
The DV-Hop localization algorithm,which uses the product of the hop count and the average hop distance to estimate distance and uses the maximum likelihood estimation method for positioning,has larger error. Aiming at the problem above,this paper proposed a developed localization algorithm OPDV-Hop,which based on the optimal hop distance and the improved particle swarm. Firstly,this algorithm adopted communication radius to revise hops between nodes. Then it optimized the current hop distance according to the average hop distance in the adjacent area of unknown nodes. Lastly,it applied the improved particle swarm algorithm to optimize the unknown node coordinates. The simulation results show that,compared with the DV-Hop algorithm,the DV-Hop algorithm based on particle swarm and the algorithm based on improved particle swarm,OPDV-Hop algorithm reduces the errors by about 18%,13% and 7% respectively. Thus,it can effectively lower down the estimation errors and improve the positioning accuracy.
出处
《计算机应用研究》
CSCD
北大核心
2017年第12期3775-3778,3783,共5页
Application Research of Computers
基金
国家自然科学基金资助项目(61372058)
