摘要
针对扩展卡尔曼滤波DV-Hop(EKF-DV-Hop)算法定位精度低和环境适应能力差的问题,提出了模糊自适应扩展卡尔曼滤波DV-Hop算法(Fuzzy Extended Kalman Filter DV-Hop,FEKF-DV-Hop).首先使用DV-Hop求解未知节点的坐标信息,然后将信标节点与未知节点之间的欧氏距离作为观测量,运用EKF算法优化未知节点的坐标,最后运用模糊控制算法建立模糊控制器(FLAC),自动调整观测噪声矩阵以适应变化的环境.仿真结果表明,该算法相比于EKF-DV-Hop算法具有更强的适应性,减少了定位偏差,提升了定位精度和环境适应力.
Aiming at the problems of low positioning accuracy and poor adaptability of EKF-DV-Hop algorithm,a fuzzy adaptive extended Kalman filter localization algorithm is proposed.Firstly,DV-Hop is used to solve the coordinate information of the unknown node,then the Euclidean distance between the beacon node and the unknown node is taken as the observation,and EKF algorithm is used to optimize the coordinates of the unknown node.Finally,fuzzy controller(FLAC)is established by using fuzzy control algorithm to automatically adjust the observation noise matrix to adapt to the changing environment.The simulation results show that the algorithm has stronger adaptability than DV-Hop algorithm,reduces the positioning error,and improves the positioning accuracy and environmental adaptability.
作者
柏植
许海峰
郭凯
李昊
BAI Zhi;XU Hai-feng;GUO Kai;LI Hao(School of Mechanical and Electronic Engineering,Suzhou University,Suzhou 234000,China)
出处
《枣庄学院学报》
2021年第5期27-33,共7页
Journal of Zaozhuang University
基金
卓越工程师培养创新项目(项目编号:szxy2020zrpc02)
校级科研平台课题(项目编号:2019ykf26,2019ykf31)
安徽省智能机器人信息融合与控制工程实验室开放课题(项目编号:IFCIR2020005).
