Design Of Indoor Positioning System Based On UWB

With the further integration of mobile communication technology and wireless sensor network,location information service technology has gradually penetrated into people’s lives.In outdoor positioning technology,Bei Dou Navigation Satellite System(BDS)has been able to meet people’s needs.In the indoor positioning technology,the positioning accuracy of ultra-wide band(UWB)indoor positioning technology can reach decimeter level,and has strong anti-interference ability and anti-multipath fading ability.However,due to the increasing complexity of the indoor environment,the impact of positioning error in the case of non-line of sight(NLOS)is gradually increasing,and its positioning accuracy will be significantly reduced.In order to solve this problem,this paper has carried out a series of research on the analysis and processing of NLOS signal in indoor positioning,mainly introducing the judgment of the range signal sight distance and NLOS degree,and further adding the process of NLOS signal processing and optimization,comprehensively analyzing the average positioning error,root mean square error(RMSE)and other relevant indicators of indoor positioning algorithm.The main research contents of this paper are as follows:1.In the complex indoor environment,the Kalman filter is not accurate in the initial state location.In the initial location,the CHAN algorithm for locating the base station group is used to solve the location equation,and then the residual weighting operation is performed on the solution results in turn.The initial state position information obtained is used as the initial value of the Kalman filter.It effectively solves the initial value problem of Kalman filter algorithm,reduces the time of initial value filter calibration,and improves the accuracy of indoor positioning.2.Aiming at the extra error of Kalman filter in complex indoor environment due to frequent change of location scene.First,the initial results are obtained by using the CHAN algorithm for locating base station packets,and then the regional confidence factor is obtained by residual normalization of the location results.Compare the confidence factor with the set threshold to get the confidence region of the location time,and finally substitute the Kalman filter gain of the confidence region for the Kalman filter iteration to get the final location result.Simulation results show that the algorithm improves the accuracy of Kalman filter positioning results.3.A real-time positioning system based on UWB is designed and implemented.Through the communication between the UWB positioning tag and the base station,the positioning information is transmitted to the upper computer for processing and analysis.The processing results are displayed and analyzed through the APP of MATLAB,and several other indoor positioning algorithms are compared to verify the advantages of this algorithm in indoor positioning.