Research On Wireless Tracking And Positioning Technology Of Mine Moving Target

In order to improve the efficiency of underground operators,timely and rapid rescue after coal mine disasters,it is necessary to carry out real-time positioning and tracking of underground mobile locomotives and operators.In this paper,the location is based on wifi wireless network,and the location fingerprint location algorithm is studied and analyzed.Firstly,introduces the widely used wireless positioning system,describes the positioning technology used in the wireless positioning system,and compares and analyzes the three positioning algorithms introduced in this paper.By comparing the three localization algorithms,the accuracy of the three algorithms is obtained in the ideal case.In this paper,the positioning scene is a mine tunnel,and the WKNN is used as the matching algorithm for fingerprint positioning.Then,the embedded track-seeking car is designed and manufactured,which can move according to the specified route.There are two wireless modules on the vehicle,which can be used to collect and transmit the wifi wireless signal to the computer terminal.In this paper,six wifi modules are selected as anchor nodes for fingerprint location,and a trace-seeking vehicle is designed and made to collect RSSI wireless signals.In this paper,a large number of RSSI signals are collected,and three filtering methods are used to analyze the RSSI data by filtering the collected wifi signals.The MATAB simulation software is used to process the RSSI signal.After a lot of data verification,the path loss model of wifi in the corridor is constructed to prepare for removing the nearest neighbor points with greater error in the following WKNN positioning algorithm.Finally,in order to solve the problem of locating and tracking moving objects in mine roadway,the improved WKNN localization algorithm and Kalman rate filtering algorithm are combined in this paper.in the region to be located,the track-seeking car is used to collect the RSSI wireless signal at the sampling point,and the collected signal is filtered by Gauss fliter,and the processed RSSI signal is constructed into position fingerprint information.In order to improve the efficiency of location algorithm in matching,this paper uses k-means clustering algorithm to divide the fingerprint database into three categories,and uses MATALB to simulate the fingerprint database.21 sampling points are selected to collect RSSI signals,each sampling point can collect 6 wifi signals.Several common matching algorithms are simulated and analyzed with the collected signals.Finally,after comparison,the WKNN matching algorithm is selected.In order to improve the positioning effect of the WKNN matching algorithm,the logarithmic distance loss model is used to eliminate the adjacent points with large errors in the WKNN matching algorithm.In order to make full use of the functions of each wifi anchor node in the WKNN matching algorithm,Each anchor node is weighted according to the Euclidean distance from the anchor node to the point to be located.The improved WKNN algorithm and Kalman filtering algorithm are combined into a hybrid localization algorithm.The simulation results show that the improved algorithm can identify the turning route of the track-seeking vehicle.The localization effect is better than that before improvement.