Indoor Positioning Based on TWR Fusion of WLS and KF
刘姝廷 1张媛媛 1张贺 1娄浩云1
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作者信息
1. 沈阳理工大学信息科学与工程学院,沈阳 110159
折叠
摘要
超宽带(ultra wide band,UWB)传感器室内定位常通过双向测距法(two-way ranging,TWR)测得基站到待测标签距离,采用三边定位法进行室内定位,但因存在非视距误差等因素影响导致定位精度低且发散,为此本文提出采用加权最小二乘法(weighted least squares,WLS)训练数据集,将其定位结果与卡尔曼滤波相结合进行室内定位.仿真实验结果表明,该方法能够解决经典定位造成较大误差的缺点,定位更快速.
Abstract
Ultra wide band(UWB)sensors often use two-way ranging(TWR)to measure the dis-tance between the base station and the target label for indoor positioning.The trilateral positioning method is used for indoor positioning,but due to factors such as non-line-of-sight errors,the positio-ning accuracy is low and scattered.Therefore,it is proposed here to use weighted least squares(WLS)to train the dataset and combine its positioning results with Kalman filtering for indoor posi-tioning.The simulation experiment results show that this method can solve the problem of large er-rors caused by classical positioning and achieve faster positioning.
关键词
超宽带/双向测距法/加权最小二乘算法/卡尔曼滤波算法
Key words
ultra wide band/two-way ranging/weighted least squares algorithm/Kalman filtering algorithm
维普
万方数据