Research On Indoor High-precision Positioning Algorithm Based On Bluetooth Low Energy

With the continuous development of the Internet of Things,locationbased services continue to enrich people’s lives,and it spawns a large number of applications in transportation and logistics management.In recent years,indoor positioning technology has developed rapidly.However,due to the complexity of indoor environment,indoor positioning technology still faces huge challenges.With the introduction of the direction finding function in Bluetooth Core Specification 5.1,the positioning technology based on the angle of arrival(AOA)can be applied to the existing Bluetooth positioning system.At present,the direction finding technology based on Bluetooth Low Energy is still affected by system noise and antenna switching,so it cannot obtain high-precision AOA information.Since the measurement error of AOA is large in the large true AOA area,the performance of the positioning algorithm based on AOA is poor in this area.In order to improve the accuracy of AOA measurement and improve the positioning performance of positioning system in the global range,this thesis proposes the indoor high-precision positioning algorithm based on Bluetooth Low Energy.Firstly,aiming at the problem that the error of AOA estimation caused by environmental noise and antenna switching,we propose an AOA estimation algorithm based on Bluetooth switching antenna array.The algorithm first processes the environmental noise through the improved wavelet noise reduction algorithm to reduce the influence of the noise on the estimation of AOA.Then,in view of the influence of phase difference error during antenna switching,we propose a phase difference processing algorithm based on unscented Kalman filter in this thesis.The I/Q signal is used to correct the phase difference information of the antenna array to improve the stability and accuracy of the phase difference.In this thesis,the simulation results verify the effectiveness of the proposed algorithm.In this thesis,we built the actual angle of arrival measurement system,and the original data is collected to calculate AOA in the real environment.The experimental results show that the accuracy of AOA calculated by the algorithm proposed in this thesis is higher than the estimation accuracy of AOA calculated from the original data,which provides a basis for realizing high-precision indoor positioning.Secondly,the proposed AOA estimation algorithm is still limited in the estimation accuracy of AOA in some special areas,which causes the problem of poor stability of the positioning accuracy of the positioning system in the global range.To solve this problem,we firstly optimize the online positioning stage of fingerprint positioning algorithm based on Received Signal Strength Indicator(RSSI),and propose a weight value algorithm that combines Euclidean distance and derived physical distance.Compared with the positioning algorithm,the improved algorithm greatly improves the performance of positioning system.Then,by comprehensively considering the characteristics of AOA positioning algorithm and RSSI fingerprint positioning algorithm,the loose coupling method is used to integrate the AOA positioning and the improved fingerprint positioning algorithm to optimize the localization effect of the local area,which improves the positioning accuracy and positioning stability in the global range of the positioning system.For moving scenes,we propose a localization algorithm based on extended Kalman filter.It fuses the localization results and then passes the extended Kalman filter to obtain positioning trajectory results in moving scenes.It further improves localization accuracy in dynamic scenes,and has good real-time performance.In this thesis,we implemented an indoor positioning system with both AOA and RSSI positioning.We analyze the function of indoor positioning system and performance of the algorithm designed in this thesis in actual scene.The experimental results show that the proposed algorithm can meet the service requirements of indoor high-precision positioning.