Research On UWB Indoor Positioning Based On Improved Error Suppression Algorithm

With the popularization of 5G internet of everything and the application of big data,indoor positioning has become a research hotspot in academic circles.On the one hand,people and things are combined with a lot of virtual data information by indoor positioning technology.This promotes the development of the internet of everything.On the other hand,the indoor positioning environment is complicated.Due to the obstruction of objects such as walls and furniture,the positioning accuracy of the outdoor satellite positioning system decreases.Based on the proposed improved non-line-of-sight error suppression algorithm,the root mean square error parameters of positioning results in typical indoor scenes are studied.The positioning accuracy is significantly improved.The main research contents and results of this thesis are as follows:(1)The research background of indoor positioning technology and the research status at home and abroad are summarized.UWB indoor channel models,common indoor positioning algorithms,machine learning classification algorithms,and positioning evaluation indicators are introduced.The computer implementation of error suppression algorithm is explained in detail.(2)Aiming at the problem of indoor positioning NLOS error,an improved error suppression algorithm is proposed.The algorithm mainly includes three modules: TDOA positioning module,NLOS environment identification module and error suppression module.Based on the residual analysis and decision algorithm,the non-line-of-sight identification module proposes adding a support vector machine to classify the positioning results.The non-line-of-sight identification algorithm is improved.The simulation results in this thesis show that the RMSE of the positioning results is effectively reduced.(3)Based on the improved error suppression algorithm proposed in this thesis,the effectiveness of the algorithm is verified.In this paper,the measurement is performed in a typical scenario.The measurement data is pretreatement and the positioning coordinate system is determined.The pretreatement data is used as the simulation input parameter of the improved algorithm.Simulation results show that the RMSE of the positioning results is effectively reduced.(4)In this thesis,positioning algorithm based on single base station is proposed.The probability distribution of multipath propagation distance data is analized.The probability distribution of the multipath propagation distance data is analyzed.An binary likelihood function for the location of the target base station is established.The model is solved and the target position probability heat map is drawn.Simulation results show that the high probability areas in the heat map are concentrated near the target location.