摘要
由于经典DV-Hop定位算法中定位精度较低,提出一种改进算法。首先,未知节点计算到各信标节点的距离时,采用不同平均每跳距离。其次,采用GSO(galactic swarm optimization)思想把网络中的信标节点分为不同种群,使用粒子群优化算法估计每个种群中未知节点的最优位置,其最优位置构成一组次优解集。最后,利用加权质心算法优化次优解集作为未知节点的坐标。实验仿真表明,该方法能有效降低未知节点的定位误差。
In order to solve DV-Hop low localization accuracy, a novel localization method was proposed. Firstly, an un- known node chose different hopsize by different beacon nodes. Secondly, according to the idea of Galactic Swarm Optimization, beacon nodes were divided into different populations in the network. Particle swarm optimization algorithm respectively estimated unknown node optimal position, thus constituting a set of sub-optimal solution. Last, unknown node coordinates equaled to weigh- ted average a set of sub-optimal solution. The experiment shows that this method decreases localization error.
出处
《仪表技术与传感器》
CSCD
北大核心
2017年第1期164-168,共5页
Instrument Technique and Sensor
基金
国家自然基金专项项目(61171190)
