College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China.
Sensors (Basel). 2023 Jun 26;23(13):5918. doi: 10.3390/s23135918.
The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.
超宽带（UWB）和惯性测量单元（IMU）定位的组合受到随机误差和非视距误差的影响，本文提出了一种改进的定位策略来解决这个问题。卡尔曼滤波器（KF）用于对原始 UWB 测量值进行预处理，抑制 UWB 测距突变值对组合定位的影响，扩展卡尔曼滤波器（EKF）用于融合 UWB 测量值和 IMU 测量值，两者的差值作为测量信息。同时使用非视距（NLOS）测量信息。根据判断结果，实时调整系统测量噪声协方差矩阵，获得最优估计，并抑制非视距因素的干扰。将当前状态的最优估计反馈到下一个状态的 UWB 测距值，对测距值进行一维滤波预处理后进行动态调整。与传统的紧耦合定位相比，本文方法在现场实验定位结果中的定位精度提高了 46.15%。
