摘要
在无线传感器网络免于测距的定位算法中,DV-Hop算法是典型算法之一;蚁群粒子群算法(ACOPSO)通常被用来作全局优化;为了降低定位误差,提高定位精度,新算法先用DV-Hop算法估量未知节点与锚节点的测量距离,蚁群粒子群算法(ACOPSO)作后期优化,最小化DV-Hop的适应度函数,从而实现基于不同的距离或路径测量方法的优化;经过Matlab仿真分析表明,在相同的仿真环境中,新算法产生的平均定位误差比DV-Hop算法和基于粒子群的定位算法产生的平均定位误差更低,有效地提高了定位精度。
DV--Hop is one of the typical localization algorithms in Wireless Sensor Network, and the hybrid of the Ant Colony Optimization and Particle Swarm Optimization （ACOPSO） is used as a global optimisation functions generally. In order to reduce the positioning error and improve the location accuracy, the new algorithm combined ACOPSO with DV--Hop, DV--Hop is used to estimate the measuring distance between unknown nodes and anchor nodes, ACOPSO is used to minimise the fitness function that related to DV--Hop, Accordingly optimize the algorithm based on different distance or path. Simulation by the MATLAB environment indicated that the new algorithm have smaller average positioning error than DV HOP algorithm and based on Particle Swarm Optimization （PSO）, it improved the location accu racy effectively.
出处
《计算机测量与控制》
CSCD
北大核心
2011年第3期732-735,共4页
Computer Measurement &Control
基金
国家"863"计划基金资助项目(2008AA01Z208)
关键词
无线传感器网络
定位算法
蚁群粒子群
DV—Hop
粒子群算法
wireless sensor network
localization algorithm
Ant Colony Optimization--Particle Swarm Optimization （ACOPSO）
DV Hop
Particle Swarm Optimization （PSO）
