A Novel Mobile Target Localization Approach for Complicate Underground Environment in Mixed LOS/NLOS Scenarios

Accurate positioning of the shearer remains a challenge for automation of the longwall coal mining process. In this paper, the popular Ultra-wideband (UWB) positioning system that has attracted considerable attention is adopted to obtain the target node location. Unfortunately, localization accuracy is still unsatisfactory and unreliable in mixed line of sight (LOS) and non-line of sight (NLOS) scenarios. To ameliorate localization accuracy of UWB for complicate underground environment where the positioning scenarios suffered from frequently switching among LOS, NLOS, and mixed LOS-NLOS condition, the novel positioning algorithm GMM-IMM-EKF was proposed. Gaussian mixed model (GMM) was employed to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters (VBAKFs) under the structure of interacting multiple model (IMM) was utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively. Then, the position of the target node was determined by exploiting extended Kalman filter (EKF) based on the outcome of IMM-VBAKF. The proposed approach was assessed by exploiting UWB P440 modules. Comparative experimental verification demonstrated that GMM-IMM-EKF strategy outperformed other positioning approaches, which can effectively reduce the adverse effect of NLOS errors and achieve higher positioning accuracy in underground environment with LOS/NLOS/LOS-NLOS transition conditions.