摘要
针对UWB定位易受多路径和非视距误差的影响,导致观测值偏大的问题,提出了一种自回归的卡尔曼滤波UWB定位方法。在实验过程中建立UWB定位卡尔曼滤波的状态方程与观测方程,再将自回归数据模型引入卡尔曼滤波,通过新息过程值中加入设定阈值并加以判断和替换来剔除测量粗差,以提高UWB测距精度。结果表明,静态定位中X、Y方向RMSE值分别提升了63.4%、46.5%,动态定位中X、Y方向RMSE值分别提升了33.3%、38.0%,利用该算法改正观测值对室内静态、动态定位精度具有显著提升效果。
Aiming at the problem that UWB positioning is easy to be affected by multi-path and non sight distance error,resulting in large observation values,we proposed an autoregressive Kalman filter UWB positioning method.In the experimental process,we established the state equation and observation equation of UWB positioning Kalman filter,and introduced the autoregressive data model into Kalman filter at first.Then,we added the set threshold to the value of innovation process,which was judged and replaced to eliminate the measurement gross error and improve the UWB ranging accuracy.The experimental results show that in static positioning,the RMSE values in X and Y direction are increased by 63.4%and 46.5%respectively,and in dynamic positioning,the RMSE values in X and Y direction are increased by 33.3%and 38.0%respectively.Using this algorithm to correct the observed values has a significant improvement effect on the indoor static and dynamic positioning accuracy.
作者
景仙林
魏永虎
JING Xianlin;WEI Yonghu(Xining Land Survey and Planning Research Institute Co.,Ltd.,Xining 810000,China)
出处
《地理空间信息》
2023年第5期117-119,共3页
Geospatial Information
基金
西宁市科技计划资助项目(2019-Y-12)。
关键词
自回归
卡尔曼滤波
UWB
非视距
阈值
autoregressive
Kalman filter
UWB
non sight distance
threshold
