一种适用于大规模无线传感器网络的定位算法

A Localization Algorithm for Large-Scale Wireless Sensor Network

针对经典多维定标的MDS-MAP算法在定位精度与矩阵计算复杂度方面的不足,提出一种基于MDS的分布式定位算法。改进后的分布式定位算法加入了分簇的思想,把网络中的节点分成不同的簇来进行局部定位,局部定位时引入Euclidean算法估算距离矩阵,再用矩阵转换将局部相对坐标图合并成全局相对坐标图,并转换为绝对坐标。仿真分析表明,提出的算法有更好的定位精度,而且在较低网络连通度和不规则网络分布的条件下表现出更好的可靠性。

A distributional localization algorithm based on multidimensional scaling （ MDS ） technique was proposed aiming at the shortages of the classic MDS-MAP algorithm in localization precision and complication of matrix computing. The improved clustering method was to build different clusters for local positioning, and Euclidean algorithm was used to calculate the distance matrix in this step. Then the local maps were combined to from a global relative coordinate map based on matrix translation, which transferred the relative coordinates to absolute coordinates. Simulation results demonstrate that the new algorithm could promote localization precision and perform well under low connectivity or anisotropic topology.