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机构地区:[1]福州大学数学与计算机科学学院,福建福州350108
出 处:《福州大学学报(自然科学版)》2012年第5期578-583,共6页Journal of Fuzhou University(Natural Science Edition)
基 金:福建省自然科学基金资助项目(2011J01345);福建省科技创新平台计划资助项目(2009J1007);福州大学发展基金资助项目(2008-XQ-23)
摘 要:提出一种基于格概率的目标定位算法,不仅可以有效消除单个传感器节点测量信号强度时存在的不确定性,还可解决检测到目标的节点数目小于4时其他方法无法解决的定位问题.同时还提出一种自学习修正方法,通过实时地修正信号衰减模型中的相关参数,可避免环境动态变化带来的定位失真.仿真结果显示,所提定位算法具有良好的定位精度和较强的抗干扰性,且受环境测量误差的影响较小.This paper has originally proposed a target location algorithm with grid - based probability (GPL) to eliminate these random factors wireless communications. This algorithm can not only elimi- nate the uncertainty of received signal strength measured by a single sensor node, but also address the location problem in case that the number of nodes which detected target is less than four which other methods cannot solve. Moreover, this paper has also proposed an adaptive learning method to improve the relevant parameters of the signal decay model so to avoid the distortion of location which is origina- ted from the dynamic change of environment. The simulation results show that our algorithm has good performance in terms of the accuracy of positioning and anti - interference compared with the related algorithms, and it is less influenced by the measure error.
分 类 号:TP393[自动化与计算机技术—计算机应用技术]
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