子图融合修正WSN节点分布式定位算法

Distributed Positioning Algorithm for WSN Nodes Based on Subgraph Partition

针对大规模WSN节点定位精度不能满足要求的问题,提出了基于误差加权和与融合修正的分布式迭代节点定位算法。算法首先将WSN表示为无向图形式,根据参考节点通信半径将其划分为多个子图,在子图内构建距离误差加权目标函数,通过相位搜索粗定位和遗传蚁群算法精定位迭代计算未知节点的精确位置信息,然后通过子图间融合对节点位置进一步修正,以加速算法收敛和提高定位精度。仿真实验表明,与现有算法相比,文中算法对参考节点的数量与通信半径要求较低,但仍可以取得最优的定位精度,且具有较好的适应性。

Aiming at the problem that the positioning precision and efficiency of large-scale WSN nodes are not high and there is a large demand for known nodes that occupy more resources,a distributed iterative node localization algorithm based on error weighted sum and fusion correction is proposed.The WSN undirected graph is firstly divided into multiple subgraphs that is based on the known node neighborhood range.And then,objective function based on the distance error weighting are established in the subgraph to iteratively calculate the node position information,and during the iteration process,the node position is further corrected by fusion among the subgraphs to accelerate the algorithm convergence and improve the positioning precision.The results of simulation experiments show that,compared with existing algorithms,the proposed algorithm can achieve the best positioning precision and time with less known node requirements or a smaller communication radius,and has better adaptability for WSN networks with different node sizes.