基于跳距与改进粒子群算法的DV-Hop定位算法

针对DV-Hop定位算法定位精度不高的问题，提出一种带改进的权重平均每跳距离与改进的粒子群算法以改进经典DV-Hop算法。一方面，提出跳距误差与估计距离误差的加权平均值，修正原始的平均每跳距离。另一方面，采用分段的指数、对数递减权重改进粒子群的权重；同时，结合人工鱼群位置更新的优点来改进粒子群算法的位置更新。用改进的粒子群算法求解未知节点坐标，以提高定位精度。实验仿真表明，该算法的定位精度和稳定性与其他算法相比有明显的改善。

DV-Hop Localization Algorithm Based on Hop-Size and Improvement Particle Swarm Optimization

In order to solve DV-Hop low localization accuracy,a novel localization method based on modified weighted average hop-size and improved particle swarm optimization algorithm is proposed. On the one hand, weighted average both hop-size error and estimated distance error modify initial average hop-size. On the other hand,index and logarithmic decrement of piecewise function improve inertia weight of PSO. Furthermore,combin?ing with localization update of Atificial Fish Swarm Algorithm improve PSO’s localization update. Then,improved algorithm estimate unknown node coordination. The experiment shows localization accuracy and stability of the method is greatly improved.