Research And System Construction Based On Passive Tag Location Method In Coal Mine

The complexity and diversity of the indoor environment bring great challenges to positioning,especially the complex and changeable environment faced by the positioning system in underground coal mines,with many interference factors such as personnel and equipment.Effective and practical positioning systems and positioning technologies are of great significance to post-disaster search and rescue personnel and daily personnel location management.In recent years,RFID technology has become a research hotspot in indoor positioning due to its low cost,wide range,non-contact,non-line-of-sight and other advantages.However,the RSSI-based RFID positioning algorithm is easily affected by the environment,resulting in low positioning accuracy and large errors.Therefore,the research on underground RFID positioning algorithm has great theoretical significance and application value.The underground RFID positioning system will be interfered by factors such as multipath and non-line-of-sight,which are the main influencing factors in the positioning process.Aiming at these two factors,this paper proposes a passive RFID positioning algorithm based on PSO-GRNN.Use GRNN to train and fit the RSSI data,and use the PSO optimization algorithm to optimize the GRNN smoothing factor,avoiding the influence of artificial adjustment of the smoothing factor,and establishing a ranging model that is more in line with the actual environment.First of all,in order to improve the positioning accuracy,this paper studies the characteristics of passive tags.Experiments verify that they are susceptible to environmental interference and mutual interference between tags in the actual environment,resulting in greater volatility.This paper uses the Kalman filter algorithm to The collected RSSI data has been filtered and optimized to filter out noise and reduce interference.Next,in order to solve the problem that the traditional ranging model is not suitable for instability in the actual situation,which leads to low positioning accuracy,a PSO-GRNN-based ranging model is proposed.The RSSI at the tag is used as the input of the network,and the output is the tag and the location to be located.The distance of the location,build the GRNN network model.The PSO optimization algorithm is introduced to optimize the smoothing factor of the network and further improve the accuracy of the model.Finally,the weighted least square method is used to calculate the target position for the ranging model proposed in this paper.Experiments are carried out in actual scenes.According to the experimental results,the algorithm in this paper has higher positioning accuracy than the traditional RSSI positioning algorithm with path loss factor.The maximum positioning error of the positioning result is 3m,and the average positioning error is 1.32 m,which can meet the needs of underground positioning.The paper contains 39 figures,5 tables and 80 references.