摘要
为提高扩展卡尔曼DV-Hop(EKF-DV-Hop)定位算法的准确性,降低其定位误差,提出了一种基于新息的改进自适应EKF-DV-Hop定位算法(Adaptive Extended Kalman Filter DV-Hop,AEKF-DV-Hop)。首先定义误差因子,通过除去对计算信标节点跳距误差大的节点进而增加跳距准确性,并应用最小二乘法估算未知节点的坐标;然后将估算的坐标和信标节点间的欧氏距离作为EKF的观测量,进一步优化未知节点坐标;最后使用自适应算法在线更新状态噪声矩阵和观测噪声矩阵。仿真对比得到,AEKF-DV-Hop对环境具有更好的适应性,定位精度有较明显的提升,增强了环境适应性。
To improve the accuracy of Extended Kalman DV-Hop(EKF-DV-Hop)localization algorithm and reduce its localization error,an improved Adaptive-EKF-DV-Hop(AEKF-DV-Hop)localization algorithm based on innovation is proposed.Firstly,The error factor is defined,and the accuracy of the hop distance is increased by removing the nodes with large error in calculating the hop distance of beacon nodes.The coordinates of the unknown nodes are estimated by the least square method.Then,the estimated coordinates and the Euclidean distance between beacon nodes are used as EKF observations to optimize the unknown nodes coordinates.Finally,the adaptive algorithm is used to update the state noise matrix and observation noise matrix online.Simulation results show that AEKF-DV-Hop algorithm has better adaptability to the environment,significantly improves the positioning accuracy and enhances the environmental adaptability.
作者
柏植
许海峰
郭凯
尹珠
BAI Zhi;XU Haifeng;GUO Kai;YIN Zhu(School of Mechanical and Electronic Engineering,Suzhou University,Suzhou 234000,China;School of Computer Science and Technology,University of Science and Technology of China,Hefei 230000,China)
出处
《宿州学院学报》
2023年第6期12-15,55,共5页
Journal of Suzhou University
基金
安徽省重点研究与开发项目(1704A0902022)
宿州学院科研平台课题(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(IFCIR2020005)。
