Research On Accurate Positioning Method Of Underground Based On UWB Technology

The natural environment in the coal mine is complex with many interference factors.It is difficult to achieve the static positioning error requirement of 30 cm.This article starts from three aspects: the selection of wireless signals,the suppression of ranging errors,and the combination of coordinate analysis algorithms.It conducts in-depth research on the existing positioning technology and positioning algorithms.It aims to reduce the underground mine positioning error,improve the positioning accuracy,and achieve achieve precise positioning.The main research contents are as follows.(1)In view of the particularity of the mine environment and considering the influence of the underground shadow effect,this thesis uses statistical methods to estimate the UWB path loss,establishes the underground UWB channel model,and obtains more accurate positioning signals.The error source in the UWB ranging process is analyzed,and the Asymmetric Double-Sided Two-Way Ranging(ADS-TWR)method with three communication processes is adopted to avoid the problem of clock asynchronous between nodes.At the same time,it effectively suppresses the equipment synchronization delay and clock frequency deviation error.(2)In order to suppress the ranging error caused by non-line-of-sight factors,the Kalman filter algorithm is selected to filter the original distance data and reduce noise.Based on the positive characteristics of non-line-of-sight errors,by imposing influence factors to limit the changes in the Kalman gain and measurement residuals,the standard Kalman filter algorithm is improved to compensate for the positive deviation caused by non-line-of-sight factors.Simulation verified that compared with the standard algorithm,the filtering effect of the optimized algorithm has been improved.(3)In order to solve the problem that the positioning performance of the Taylor series method depends on the accurate initial value,the positioning mechanism of the traditional Chan-Taylor collaborative algorithm is used for reference.A positioning algorithm(P&T algorithm)combining particle swarm algorithm and Taylor series is proposed,which initially estimates the target position through particle swarm algorithm,and then performs secondary positioning by Taylor series.The performance of the algorithm is analyzed and evaluated through the root mean square error.The simulation results show that the P&T algorithm can obtain more accurate positioning results.In a noisy environment,its positioning performance is better than the Chan-Taylor collaborative positioning algorithm.(4)In order to improve the search ability and convergence speed of the standard particle swarm algorithm,the linearly decreasing inertia weight is reconstructed.The improved inertia weight can be adaptively changed,and the global search ability and local search ability of the particles are further balanced.The joint algorithm of adaptive particle swarm algorithm and Taylor series(AP&T algorithm)achieves better positioning performance,and the positioning accuracy of the algorithm is further improved.The simulation experiment shows that the positioning deviation of the target node position obtained by the AP&T algorithm fluctuates within a range of 30 cm,which meets the national centimeter-level precise positioning requirements.This thesis has 31 pictures,12 tables,and 99 references.