基于Euclidean修正的分布式加权定位算法

Distributed weighted node localization algorithm based on Euclidean correction

传统的多维定标(MDS)算法由于采用多跳距离代替节点间的直接距离,生成的局部网络准确度低,在不规则网络中定位误差大。相对于现有的算法,引入Euclidean方法来产生多跳节点间的准确距离,并采用一种加权机制来改进协强系数,以抑制累积误差。仿真结果表明该方法在C型网络和低连通度的矩形网络定位中能取得更好的效果。

The traditional Multi-Dimensional Scaling（MDS） algorithm adopts multi-hop distance to replace direct distance,resulting in low accuracy of the local network and large localization error in irregular network.Relative to the existing algorithms,the paper introduced the Euclidean algorithm to generate accurate multi-hop distance between nodes,and used weighting mechanism to improve the coefficient of stress.The simulation results show that in low connectivity rectangle network and C-shape network localization,this method achieves better performance.