基于最短路径修正的多维定标定位算法  

Improved multi-dimensional calibration algorithm based on shortest path correction

作　　者：邬春明[1] 杨雪 李二磊 Chunming;YANG Xue;LI Erlei(School of Information Engineering, Northeast Electric Power University, Jilin 132012, China;School of Information Science and Technology, Dalian Maritime University, Dalian 116026, China)

机构地区：[1]东北电力大学信息工程学院,吉林吉林132012 [2]大连海事大学信息科学技术学院,辽宁大连116026

出　　处：《南京邮电大学学报（自然科学版）》2018年第2期87-91,共5页Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition

基　　金：国家自然科学基金(61501107)资助项目

摘　　要：当无线传感器网络节点密度较低或节点分布不均匀时,利用多维定标定位算法求出的最短路径与节点实际距离有一定误差。针对这个问题提出了一种基于最短路径修正的改进算法。根据节点的局部密度对无线传感器网络中节点间的边进行重新赋值,计算节点间的距离。结合人工蜂群智能算法选出节点间的最优最短路径,计算出节点间距离矩阵。实验仿真结果表明,该改进算法的定位精度相对于经典集中式多维定标算法提高了11%左右。The certain error between the shortest path distance of nodes and the actual Euclidean distance of nodes exists when the wireless sensor network node is non-uniform or the node density is low. Aimed at this problem, an improved algorithm based on the shortest path is proposed. The distance between the nodes is calculated by reassigning the edges in the sensor network connection diagram according to the lo- cal density of the nodes. Combined with the intelligent artificial bee colony algorithm to select the optimal shortest path between nodes, the distance matrix between nodes is calculated. Simulation results show that the accuracy of the improved algorithm is about 11% higher than that of MDS_MAP algorithm.

关 键 词：多维定标 最短路径修正 节点密度 人工蜂群 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]