摘要
针对无线传感器网络节点定位问题,提出了一种自适应罚函数优化粒子群的算法。算法在定位过程中,首先运用极大似然估计法进行粗略定位,然后通过对计算误差和测距误差之间差值进行加权处理,限制搜索区域,根据群体中可行解比例的大小,自适应调节罚因子的大小进行迭代寻优,最终得到节点坐标。仿真结果表明:算法较好地克服了传统粒子群算法收敛速度慢,易陷入局部极小点等问题,对比同类算法,算法具有更高的定位精度和较快的收敛速,且稳定性更高。
Aiming at the problem of wireless sensor network node localization,this paper proposes an adaptive penalty function optimization algorithm for particle swarm optimization. In the course of positioning,the maximum likelihood estimation method is used for the rough positioning first,and then it applied the weighting the difference between the calculation error and the ranging error to restrict the searching area. Finally,according to the proportion of the feasible solution in the population,iterative optimization is performed by adaptively adjusting the size of the penalty factor to obtain the nodal coordinates. The simulation results show that the proposed algorithm can overcome the problems of slow convergence speed of traditional particle swarm algorithm and easy to fall into local minimum point. Compared with the similar algorithms,this algorithm has higher positioning accuracy,faster convergence speed and more stability.
作者
刘宏
韩亚波
张时斌
关业欢
LIU Hong ＊,HAN Yabo,ZHANG Shibin,GUAN Yehuan(School of Electrical Engineering and Automation,Jiangxi University of Science and Technology,Ganzhou Jiangxi 341000,China)
出处
《传感技术学报》
CAS
CSCD
北大核心
2018年第8期1253-1257,1265,共6页
Chinese Journal of Sensors and Actuators
基金
国家自然科学基金项目(61163063)
关键词
无线传感器网络
自适应罚因子
粒子群算法
节点定位
RSSI测距
wireless sensor network
self-adaptive penalty function
particle swarm optimization
node localization
RSSI ranging
