Xie Changping, Fang Xinjian, Yang Xu
School of Geomatics, Anhui University of Science and Technology, Huainan 232001, China.
The Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters, Huainan 232001, China.
Sensors (Basel). 2024 Nov 11;24(22):7213. doi: 10.3390/s24227213.
To improve the current indoor positioning algorithms, which have insufficient positioning accuracy, an ultra-wideband (UWB) positioning algorithm based on the Levenberg-Marquardt algorithm with improved Kalman filtering is proposed. An alternative double-sided two-way ranging (ADS-TWR) algorithm is used to obtain the distance from the UWB tag to each base station and calculate the initial position of the tag by the least squares method. The Levenberg-Marquardt algorithm is used to correct the covariance matrix of the Kalman filter, and the improved Kalman filtering algorithm is used to filter the initial position to obtain the final position of the tag. The feasibility and effectiveness of the algorithm are verified by MATLAB simulation. Finally, the UWB positioning system is constructed, and the improved Kalman filter algorithm is experimentally verified in LOS and NLOS environments. The average X-axis and the Y-axis positioning errors in the LOS environment are 6.9 mm and 5.4 mm, respectively, with a root mean square error of 10.8 mm. The average positioning errors for the X-axis and Y-axis in the NLOS environment are 20.8 mm and 18.0 mm, respectively, while the root mean square error is 28.9 mm. The experimental results show that the improved algorithm has high accuracy and good stability. At the same time, it can effectively improve the convergence speed of the Kalman filter.
为了改进当前定位精度不足的室内定位算法，提出了一种基于带有改进卡尔曼滤波的列文伯格-马夸尔特算法的超宽带（UWB）定位算法。采用交替双边双向测距（ADS-TWR）算法获取UWB标签到每个基站的距离，并通过最小二乘法计算标签的初始位置。利用列文伯格-马夸尔特算法校正卡尔曼滤波器的协方差矩阵，并使用改进的卡尔曼滤波算法对初始位置进行滤波，以获得标签的最终位置。通过MATLAB仿真验证了该算法的可行性和有效性。最后，构建了UWB定位系统，并在视距（LOS）和非视距（NLOS）环境中对改进的卡尔曼滤波算法进行了实验验证。在LOS环境中，X轴和Y轴的平均定位误差分别为6.9毫米和5.4毫米，均方根误差为10.8毫米。在NLOS环境中，X轴和Y轴的平均定位误差分别为20.8毫米和18.0毫米，而均方根误差为28.9毫米。实验结果表明，改进后的算法具有较高的精度和良好的稳定性。同时，它可以有效提高卡尔曼滤波器的收敛速度。
