Improving the Localization Accuracy and Robustness of a UWB System Using VB-CSRUKF and RTS in Harsh Underground NLOS Environments

Accurate and reliable location estimation of the shearer remains a crucial challenge to the realization of automated and unmanned mining. The poor positioning accuracy of conventional methods produces limited applicability owing to non-line-of-sight (NLOS) ranging errors, and the location errors of anchor nodes (ANs) and the velocity of the mobile target node (TN) are rarely considered. To tackle these issues, this article presents a new positioning strategy integrating calibration, the variational Bayesian (VB), constrained square root-unscented Kalman filter (CSRUKF), and robust Taylor series (RTS) algorithm to improve the ultra-wideband (UWB) positioning system accuracy in harsh underground coal mine environments. First, more accurate ANs position coordinates can be obtained using the proposed compensation method with the aid of reference nodes (RNs) under line-of-sight (LOS) conditions; then, the least squares (LSs) method is implemented to return the location of the TN using the compensated coordinates and corrected distance. Secondly, the VB-CSRUKF technique, which takes into account of the time-variant measurement noise and sigma points violating the constraints in the square root unscented Kalman filter (SRUKF), is used to eliminate the adverse effects of the NLOS error. Finally, the RTS method, in which robustness and the velocity of the TN in Taylor series are embedded, isadopted to further enhancethe final estimation accuracy. The experimental results corroborated that the developed VB-CSRUKF-RTS technique can efficiently and effectively ameliorate the overall accuracy, and is able to yield more accurate position estimation as compared with other approaches, possessing the high robustness and prominent performance.