Precise personnel positioning method in underground mine based on grey prediction model

Abstract

The positioning accuracy of the precise personnel positioning system in underground mine is affected by the non-line-of-sight error and clock error. At present, the system mostly uses Kalman filter-based positioning method to reduce the error, but the positioning accuracy is not high when there is gross error in the measured data. In order to solve this problem, a precise personnel positioning method in underground mine based on grey prediction model is proposed. When a person carrying a marker card enters the coverage area of the positioning reader, the positioning reader calculates the measured distance between the marker card and the reader through wireless positioning technology and stores the measured distance into the data cache area. According to the measured distance in the data cache area, the GM (1, 1) model is used to calculate the predicted distance between the marker card and the reader at the next moment. When the prediction accuracy level of this predicted distance is excellent and the difference with the measured distance exceeds the error judgment threshold, the predicted distance is used to replace the measured distance to achieve the optimal compensation of the distance measurement error. The test results show that the method is not affected by the distance measurement error. When there is a gross error in the measured distance, the positioning accuracy of this method is significantly better than that of the Kalman filter-based positioning method.

Keywords

• underground personnel positioning; precise positioning; grey prediction model; gross error