针对当前UWB技术在传统厂区布站方式下室内定位误差较大,无法良好实现对象的三维显示问题,基于多传感器融合,本文提出一种将UWB数据与IMU数据进行先预处理、后融合的算法。该方法通过TOF测距后将UWB数据进行小波阈值去噪,递推最小二乘定位,并与IMU数据进行扩展卡尔曼滤波融合,从而减小标签的三维距离误差,提高定位精度,实现对厂区可移动设备的实时监控管理。仿真结果表明,基于多传感器融合的RLS-EKF室内定位能够减小标签三维定位误差,提高定位的准确性,满足厂区室内实时定位的精度要求。

In view of the current UWB technology in the traditional plant layout mode indoor positioning error is large,can not achieve a good three-dimensional display of objects,an algorithm based on multi-sensor fusion is proposed,which preprocesses UWB data and IMU data before fusion.In this method,after the TOF ranging,the UWB data is denoised by wavelet threshold,and the recursive least squares localization is performed,and the extended Kalman filter is fused with the IMU data.In this way,the three-dimensional distance error of the label can be reduced,and the positioning accuracy can be improved,so as to achieve the purpose of real-time monitoring and management of the movable equipment in the factory.The simulation results show that the RLS-EKF indoor positioning based on multi-sensor fusion can reduce the three-dimensional positioning error of tags,improve the accuracy of positioning,and meet the accuracy requirements of indoor real-time positioning.