【摘要】 在分析经典DV-Hop算法的定位误差与节点之间的平均跳距、节点之间跳数关系的基础上,提出了一种基于跳距可信度的跳距加权和跳数优化的粒子群算法。跳距可信度为节点间实际距离除以节点通信半径的值与节点之间跳数的比值,利用跳距可信度来对节点的平均跳距进行修正;并通过跳距可信度计算出修正因子来对跳数进行优化。最后用改进的粒子群算法代替最小二乘法来对定位位置进行优化。实验结果表明:本文算法的定位精度相对于经典DV-Hop算法和BDV-Hop算法分别提高了约12%和2.4%。更多还原

【Abstract】 Abstrac: On the basis of analyzing on localization error of classical DV-Hop algorithm and average hop distance between nodes and the hop count between nodes,a particle swarm optimization( PSO) algorithm based on hop distance-weighted of hop distance credibility and hop-count optimization is proposed. Hopping credibility is the ratio of the actual distance between nodes divided by the value of node communication radius to the hopcounts between nodes,and use hop distance credibility to correct the average hop distance of node. According to credibility of hop distance is used to calculate correction factor to optimize the hopcounts. Finally,improved PSO is used to optimize the positioning location instead of least square method. Experimental results show that compared with the classical DV-Hop algorithm and the BDV-Hop algorithm,localization precision of the proposed algorithm is improved about 12 % and 2. 4 %,respectively.更多还原