A precise location method for mine personnel based on residual estimation Kalman filtering

Passive moving target localization is one of the key technologies for the safety of operators in underground spaces of mines and timely rescue after disasters. The current algorithm for locating personnel in mines has problems such as low positioning accuracy and susceptibility to non-line-of-sight (NLOS) error interference, etc. In this paper, we propose a method for accurate positioning of mine personnel based on residual estimation Kalman filtering. Firstly, a dual-range TOA ranging technique is used to effectively suppress the measurement errors caused by equipment time asynchrony in signal transmission; then the measured data are pre-processed using the trend shift method to suppress the noise of small probability and large interference in the measured signal; finally, a Kalman filter introducing the idea of residual estimation is designed to overcome the effects of nanosecond dense multipath signals and background noise existing in the mine, thus The accuracy of the measurement of the personnel position in the mine tunnel is improved. Simulation experiments show that the proposed method has strong robustness in the tunnel environment, and the localization accuracy is significantly improved, reducing the impact of non-visual error noise and dense multipath effect on the performance of the algorithm.