摘要
针对无线传感器网络中蒙特卡罗移动节点定位算法的不足,提出了一种采样优化的蒙特卡罗移动节点定位算法。该算法根据运动连续性,利用曲线拟合方法,得出样本节点位置后验密度分布取值较大的区域,对该区域内样本节点的权值进行优化,从而完成未知节点的定位。仿真结果表明,改进后的算法能够显著地减少定位所需的样本数,有效提高了无线传感器网络移动节点定位的准确性和鲁棒性。
In view of the deficiencies of Monte Carlo localization algorithm in mobile wireless sensor networks,a new localization algorithm featuring sampling optimization Monte Carlo is introduced.According to the continuity of movement to carry out curve fitting,a region where the value of posterior density distribution is large is calculated,and the sample weights are optimized.Simulation results indicate that the improved algorithm needs fewer samples,the accuracy and robustness of target locating in wireless sensor networks can be improved effectively.
出处
《系统工程与电子技术》
EI
CSCD
北大核心
2010年第9期2001-2004,共4页
Systems Engineering and Electronics
基金
国家自然科学基金(60974082)资助课题
关键词
移动无线传感器网络
蒙特卡罗定位算法
曲线拟合
采样优化
mobile wireless sensor network
Monte Carlo localization algorithm
curve fitting
sampling optimization
