Angle Residual Weighted Adaptive Kalman Filtering for Mine-Used Underground Monorail Crane Localization Based on UWB/INS Integration

The fusion of inertial navigation systems (INS) and ultrawideband (UWB) is a primary approach to enhance the positioning accuracy and stability of mine-used underground monorail cranes (MUMC) in coal mine tunnels. UWB demonstrates high positioning accuracy in ideal conditions. It is, however, susceptible to nonline-of-sight (NLOS) effects. Meanwhile, INS is unaffected by external environmental conditions but suffers from cumulative errors. To address these challenges and enhance the positioning accuracy and stability of MUMC, this article proposes an angle residual weighted adaptive Kalman filtering (ARWAKF) for MUMC positioning based on UWB/INS integration. The angular residuals are designed to assign weights to INS and UWB in the fusion algorithm, which rely on the geometric relationship of the UWB network and the motion state of MUMC. Experimental results demonstrate the superior performance of the proposed positioning method compared to INS, UWB, and extended Kalman filtering (EKF).