基于图优化和EKF的UWB组合定位算法
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1.广东工业大学机电工程学院,广州 510006;2.佛山科莱机器人有限公司,佛山 528225

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TP212.9

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广东省重点实验室项目(2017B030314178);广东省联合培养研究生示范基地(粤教研函[2021]2号)


UWB combined location algorithm based on graph optimization and EKF
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1.School of Electromechanical Engineering,Guangdong University of Technology,Guangzhou 510006,China;2. Foshan Clev Robot Co., Ltd., Foshan 528225, China

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    摘要:

    针对超宽带(UWB)技术受电气转换时间延迟,天线延迟以及基站间时间同步等因素的影响而导致UWB传感器的测量值存在误差的问题,本文提出一种基于图优化和EKF的UWB组合定位算法,首先使用EKF算法得到定位初值,接着构建包含UWB测距误差的目标函数,然后通过图优化算法求解使整个定位过程UWB总体测距误差最小的基站位置,然后将求解的基站位置代入EKF算法进行第二次计算,得到更为精确的定位结果,最后应用Mean-Shift算法对定位结果进行聚类分析。实验结果表明:本文提出算法相对于EKF算法和最小二乘法,水平定位精度平均提高了27%和38%,而且得到的目标运动轨迹平滑性远优于传统定位算法。

    Abstract:

    Aiming at the problem that UWB sensor measurement errors are caused by electrical conversion time delay and antenna delay in UWB technology, a UWB combined positioning algorithm based on graph optimization and EKF is proposed in this paper. Firstly, EKF algorithm is used to obtain the initial positioning value, and then the objective function including UWB ranging error is constructed. Then, the graph optimization algorithm was used to solve the base station location that minimized the overall location error of UWB in the whole positioning process. Then, the solved base station location was substituted into EKF algorithm for the second calculation to obtain more accurate location results. Finally, Mean-Shift algorithm was used to perform cluster analysis on the location results. Experimental results show that compared with the EKF algorithm and the least square method, the horizontal positioning accuracy of the proposed algorithm is improved by 27% and 38% on average, and the obtained target trajectory smoothness is much better than the traditional positioning algorithm.

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易颂轩,高伟强,刘建群,郭俊权,刘 达.基于图优化和EKF的UWB组合定位算法[J].电子测量技术,2022,45(23):1-6

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  • 在线发布日期: 2024-03-08
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