LAUSR

The achievement of accurate and reliable localization is invariably a predominant bottleneck to the realization of shearer automation. Unfortunately, the positioning accuracy may significantly degrade and the yield may be unsatisfactory due to the existence of the line-of-sight (LOS), non-line-of-sight (NLOS) along with mixed LOS-NLOS situations in complicated under-ground environments. To address this issue, this article proposes a novel localization algorithm called GMM-NNIMM-CLMFO, which is a combination of the Gaussian mixture model (GMM), neural network (NN)-based interacting multiple model (NNIMM), Caffery localization (CL), and moth-flame optimization (MFO). The GMM is first used to re-estimate the range, and the variational Bayesian cubature Kalman filter with the assistance of NN under the IMM framework is then respectively employed to diminish the LOS and NLOS errors. Then, based on the result of GMM-NNIMM, the CL method is used to compute the position coordinates of the target node. Finally, the MFO algorithm is implemented to optimize the positioning outcome sourced from the GMM-NNIMM-CL method. The proposed method is evaluated by using P440 sensors, and the experimental results demonstrate that the proposed technique is able to remarkably enhance the overall positioning accuracy, and outperforms other approaches. It can therefore efficiently alleviate the influence of NLOS errors and achieve more accurate position estimation, thereby manifesting better positioning performance and higher robustness in mixed LOS/NLOS situations.