锚节点稀疏的传感器网络节点自定位算法

Self-localization Algorithm for Sensor Networks of Sparse Anchors

针对Euclidean算法中定位精度及覆盖率受锚节点密度影响较大的问题,提出一种改进的分布式节点自定位算法。该算法将初始定位精度较高的节点升级为锚节点,未知节点根据更新的锚节点位置信息循环求精,并通过估计坐标值的方差来控制循环求精过程中的循环次数。仿真实验显示,改进定位算法在锚节点密度较低的情况下能有效提高定位精度和覆盖率,明显降低了对锚节点密度的依赖程度。

To address the problem that anchor ratio has a strong impact on localization error and coverage in Euclidean algorithm, this paper proposes an improved distributed localization algorithm. This method uses high localization accuracy nodes as new anchor nodes. According to the information, other nodes raise the localization accuracy by iterative refinements. And it controls the circulation times with the variance of coordinates. Simulation results show that, compared with Euclidean algorithm, improved localization algorithm can enhance the localization accuracy efficiently when the anchor ratio is lower. It is obvious that the influence of anchor ratio on the localization accuracy and coverage is less.