摘要
研究监测空间节点定位精度问题,由于无线传感器网络基于测距节点定位存在误差,造成精确度低。为此采用一种分群式粒子群优化算法(GPSO),对利用TOA的极大似然估计定位法估算出的结果进行优化处理。通过仿真从测距误差、未知节点个数和节点通信半径三方面分析三种算法的平均定位误差。结果表明,分群式粒子群算法能够提升标准粒子群算法跳出局部最优的能力,有效地避免了标准粒子群优化算法易早熟、陷入局部最优的缺点,最大概率地寻找到全局最优解,最终提高了节点定位精度。
In view of some problems such as large error of position and low accuracy existing in the wireless sen- sor network（WSN） based on ranging localization algorithm, a group particle swarm optimization（GPSO） algorithm was used to optimize the result of the maximum likelihood estimation method based on TOA. Baed on simulation, three algorithms were analysed from three aspects of average position errors, including the ranging error, unknown nodes and node communication radius. The simulation results show that, the GPSO can enhance PSO＇s ability to jump out of the local optimum and the maximum probability of searching for global optimal solution. At the same time, it can effectively avoid the shortcomings of the standard particle swarm optimization algorithm. Finally, the node location accuracy was improved.
出处
《计算机仿真》
CSCD
北大核心
2014年第2期370-373,共4页
Computer Simulation
基金
国家自然科学基金(60974005)
