MTOA positioning method of coalmine based on Kalman filter and parameter fitting

The problem that the positioning accuracy of time of arrival (TOA) positioning method is susceptible to the delay of the non line of sight (NLOS) in propagation makes it difficult to meet the requirements of underground emer- gency rescue,personnel operation management and mine IoT (Internet of things) construction,etc. Based on the anal- ysis of the reference model and the characteristics of the mine roadway equipment,the NLOS propagation delay of the roadway electromagnetic wave is divided into random NLOS delay and fixed NLOS delay. Combined with the character- istics of two kinds of NLOS delays,a method of mine TOA positioning based on improved Kalman filter and parameter fitting is proposed. In order to eliminate the random NLOS delay error in roadway caused by mobile equipment,such as locomotives in mine roadway,and irregular equipment and features by randomness and difficulty in quantitative analysis,a new threshold is designed and introduced into the Kalman filter to improve the system's ability in filtering the pulse error. Meanwhile,in order to suppress the stable NLOS delay error caused by the fixed facilities and equipment in the mine roadway,the roadway range finding error model is proposed,in which the functional relationship between the inherent equipment parameters and the positioning estimated value is established. Thus,the positioning accuracy of the system can be improved by using the parameter fitting and projection geometry algorithm. The simulation results show that after the measurement data is processed by the Kalman filter based on the threshold of innovation,the error curve tends to be smoother,and the positioning error is kept between 1. 9 and 3. 1 m. After being processed by the pa- rameter fitting and geometric algorithm,the positioning error is between 0 and 0. 8 m,and the average error is reduced from 2. 4 m to 0. 3 m. Compared with the other three methods,including the symmetric double-sided two-way ranging (SDS-TWR) method,the Kalman filter and fingerprint localization method,and the Kalman filter and parameter simu- lation,the average positioning error of the proposed method is reduced by 3. 4 m,0. 4 m and 0. 6 m respectively. It shows that the proposed method has a significant inhibitory effect on the TOA positioning error,and can effectively im- plement the TOA method in the mine NLOS environment.