Design a Novel Method to Improve Positioning Accuracy of UWB System in Harsh Underground Environments

Accurate positioning is a necessary prerequisite for the realization of intelligent and autonomous mining. Although most research efforts have focused on localization techniques, these methods are incapable of producing a sufficiently high and reliable location estimation accuracy in harsh underground coal mine environments. To enhance the positioning accuracy of the target node (TN), this article proposes an innovative method denoted as MCVBUKF-RTS-ALO by integrating the maximum correntropy unscented Kalman filter (MCUKF), variational Bayesian (VB) methodology, Rauch–Tung–Striebel (RTS) smoother, and ant lion optimizer (ALO) algorithm. First, the MCVBUKF-RTS method is proposed, taking into account complex measurement noise and abnormal measurement data to enhance the ranging accuracy due to the existence of inevitable uncertainties during practical implementation. In particular, the MCVBUKF is performed during the application of the RTS smoother for the forward filtering and backward smoothing to alleviate the influence of corrupted measurements. Following this, the robust weight total least squares is adopted to estimate the TN's location, and the ALO is subsequently performed to further optimize the estimated results. An experimental investigation was implemented to validate the practicability and effectiveness of the designed method using the ultra-wideband (UWB) system. The experimental results demonstrate that the designed MCVBUKF-RTS-ALO method can greatly improve the positioning accuracy of the UWB system and substantially outperforms the other state-of-the-art-methods.