Abstract
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.
Highlights
The shearer, a key equipment of a fully mechanized mining face (FMMF), played an important role in the coal production process
It was straightforward to see that the positioning accuracy of the Gaussian mixed model (GMM)-least square (LS), GMM-two-stage Maximum Likelihood (TSML), and GMM-extended Kalman filter (EKF) were improved by 19.71%, 9.12%, and 14.34% on average localization error compared with LS, TSML, and EKF, respectively, that for GMM-interacting multiple model (IMM)-LS, GMM-IMM-TSML, and GMM-IMM-EKF were improved by 32.12%, 23.58%, and 60.41%, respectively
By adopting GMM algorithm to eliminate the effect of LOSNLOS situation and two parallel self-adjusting variational Bayesian adaptive Kalman filters (VBAKFs) under the framework of IMM technique to alleviate the line of sight (LOS) and non-line of sight (NLOS) errors, respectively, the measured distances between target node and corresponding anchor nodes (ANs) can be more accurately re-estimated for frequent transitions between LOS, NLOS, and LOS-NLOS situation
Summary
The shearer, a key equipment of a fully mechanized mining face (FMMF), played an important role in the coal production process. Above mentioned approaches of target localization in line of sight (LOS) condition were difficult to yield satisfactory accurate position estimation and generated unreliable final estimation results due to non-line of sight (NLOS) error. In this paper, to reduce the measurement error and enhance the localization accuracy, a novel localization approach framework was proposed to handle with the dynamically changing propagation channel between ANs and the moving target node for underground environment with frequent transition of LOS/NLOS/LOS-NLOS scenarios. (1) We proposed a novel approach to deal with the frequently changing propagation channel among LOS, NLOS and LOS-NLOS scenarios for the underground environment. (2) We used the GMM-based algorithm for calculating the initial state probabilities of the LOS and NLOS condition, and eliminating the interference of LOS-NLOS situation, which was beneficial to obtain the more accurate measured distances between the target node and corresponding AN. Experimental results manifested that the NLOS error and localization accuracy can be significantly ameliorated with high robustness
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