Research On Accurate Location Algorithm Under Sparse Location Base Station In Underground Coal Mine

Wireless accurate positioning technology is an important technical support for coal mine safety production.From the perspectives of mine construction economy,maintainability and application of integrated technology of underground communication and positioning,it can be predicted that the density of base stations in some scenes of underground coal mine is inevitably sparse.In the scenario of sparse positioning base stations,positioning information is scarce,leading to the problem of low positioning accuracy.It is of great significance to study the positioning method suitable for the sparse environment of underground base station and improve the positioning accuracy of sparse base station scene,so as to consolidate the foundation of coal mine production safety.Aiming at the particularity of location in sparse scene of base station in underground coal mine,this thesis studies the improvement of location accuracy.Due to the complex and varied underground environment of coal mine and sparse base stations,the quality of limited positioning information is not high and the positioning accuracy is affected.In this thesis,a data preprocessing algorithm based on sparse coding and extended Kalman filter fusion is proposed.Firstly,the dictionary learning algorithm is used to generate the regional dictionary cluster by using the prior channel state information,and the dictionary cluster and the feedback signal after sending the pilot matrix are input into the orthogonal matching tracking algorithm to reconstruct the precise channel impulse response.Then,in the data update stage of extended Kalman filter,the channel impulse response reconstructed by sparse coding is weighted with the channel impulse response predicted by extended Kalman filter,so as to improve the accuracy of channel impulse response estimation.Finally,the two dimensional joint estimation algorithm is used to estimate the arrival Angle and flight time of the corresponding position.Simulation results show that the proposed algorithm can improve the quality of location information.The integration of communication and positioning technology limits the number of positioning base stations deployed in some downhole scenarios,resulting in insufficient positioning information available and affecting positioning accuracy.In this thesis,based on the analysis of the advantages and disadvantages of existing sparse base station scene location models,a DTOF-AOA single base station location model based on scatterer information is proposed.The multi-path effect in the roadway is utilized to obtain the information of flight time difference and arrival Angle required for positioning and reduce the influence of multi-path effect in downhole environment.Since the number of algorithm solutions increases exponentially with the number of scatters,the particle swarm intelligent optimization algorithm is used to solve the target location,and the accurate location is realized in the sparse scene of the underground positioning base station.The robustness of the proposed localization algorithm and the accuracy of the localization results are verified by simulation.There are 34 figures,10 tables and 84 references in this thesis.