Optimization study for positioning and tracking personnel in coal mines based on LO-EKF algorithm

When Wireless Sensor Network (WSN) is applied to personnel positioning in mine,because of the influence of multipath interference and mechanical & electrical noise,its location accuracy is usually not high.To optimize the anti-interference technology used in conventional positioning and tracking algorithms,this paper proposes an optimized Extended Kalman Filtering algorithm (LO-EKF) for positioning workers in the mine.This algorithm combines the inertial measurement unit (IMU) with the weighted centroid localization algorithm (WCL) to estimate the parameters of the states of target workers,and then processes and optimizes these parameters through an extended Kalman filter (EKF) configured by statistical covariance matrix ,in order to get final target position.Simulation results show that,compared to location errors of the cumulative distribution function(CDF),LO-EKF algorithm with respect to the traditional EKF algorithm not only protect the short-term high accuracy,but also improve the long-term accuracy.