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

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. INDEX TERMS Ultra-wideband, variational Bayesian adaptive Kalman filter, Gaussian mixed model, interacting multiple model, underground environment.

A cascaded algorithm based on GMM, GKF, and data fusion for mobile tracking in wireless sensor network

“…Based on the special characteristic of GMM, it is introduced to deal with the frequent changes of transmission state; furthermore, it uses the GMM‐based algorithm in the Markov‐based system framework instead of fixed transition probabilities 28 . Two parallel variational Bayesian adaptive Kalman filters (VBAKFs) 29 are used to smoothen the output of GMM; it also uses GMM probability instead of fixing state probabilities compared with the aforementioned paper 28 . The author proposed a positioning system based on GMM soft clustering, and random decision forest 30 can provide room‐level and latitude‐longitude prediction.…”

A TDOA and PDR Fusion Method for 5G Indoor Localization Based on Virtual Base Stations in Unknown Areas

“…K. Han [35] et al established binary and improved multiple support vector classification models to realize NLOS intrusion detection and high-discrimination fingerprint localization, respectively. B. Cao [36] et al employed Gaussian mixed model to re-estimate the measurement distance, and two parallel variational Bayesian adaptive Kalman filters under the structure of interacting multiple models were utilized to smoothen the result of GMM to eliminate the LOS and NLOS errors, respectively. G. Wang [37] et al proposed to jointly estimate the source position and the NLOS error in the reference path.…”

Identifying Balls Feature in a Large-Scale Laser Point Cloud of a Coal Mining Environment by a Multiscale Dynamic Graph Convolution Neural Network

“…It has the advantages of a fast transmission speed and a low power consumption. 15,16 However, in the coal mine working face, due to the high equipment density, the reflection, refraction, and diffraction of the metal surface on the pulse signal are serious, resulting in a low refresh rate and increased power consumption, which affects the range and accuracy of ultra-wide-band sensing. 17,18 The inertial navigation method is a completely autonomous sensing system.…”