【摘要】 传感器节点的位置信息在无线传感器网络的监测活动等应用中起着至关重要的作用,而实现节点定位较好的方法是采用定位算法进行估计,因此定位算法的研究是目前热门的研究课题之一.本文主要研究分析了分布式加权多维标度定位算法,针对其不能适应网络连通度变化、网络拓扑不规则且收敛速度较慢等不足,提出了一种改进算法.该算法采用的加权机制与邻居选择机制综合考虑1跳邻居数目、节点自身定位精度与测距误差,并且引入最速下降法优化其目标代价函数.实验结果表明:在相同的实验环境下改进算法与原算法相比,在定位精度提高的情况下对不规则、大规模网络有很好的适应性且有更好的鲁棒性.更多还原

【Abstract】 Location information has played an increasingly important role in many applications of wireless sensor networks,such as monitoring activities and so on.And a better way to obtain location information is to use the localization algorithm.So the research of localization algorithm is one of the hot research topic.In this paper,through the analysis of the distributed weighted multidimensional scale localization algorithm,we propose an improved algorithm,which makes up for the shortage that this algorithm can not adapt to the network connectivity change,the irregular network topology and the convergence speed of this algorithm is slow.We use the weighted mechanism and neighbor choice mechanism that comprehensively considerate the number of one hop neighbors,the node positioning precision and the location error.We also introduce the steepest descent method to ptimize the goal cost function.Simulation results show that in the same experiment environment,compared with the original algorithm,improved algorithm proposed in this paper improves the localization accuracy,robustness and is more adaptable to the irregular topology,large-scale network.更多还原