Research On Indoor Positioning Algorithm Based On Bluetooth RSSI

In recent years,location-based services(LBS)have emerged in the sight and gained widespread attention.With the abundance of indoor activities and the growth of people’s time indoors,the need for indoor positioning technology is becoming more and more obvious.In this thesis,we design and implement a localization algorithm based on Bluetooth Received Signal Strength Indicator(RSSI)for indoor environments.In this thesis,we build an experimental environment,collect the RSSI of Bluetooth beacons with the same parameters,establish a unified indoor RSSI logarithmic fading propagation model in this environment,find the parameters of the propagation model based on Bluetooth RSSI by the gradient descent method,evaluate the fit of the propagation model by using the R-squared parameter in the regression model,and verify the effectiveness of the established fading propagation model in the indoor environment.Since the signal is affected by obstacle interference and multipath effect during propagation,the RSSI value at the same location varies randomly.Therefore,in this thesis,the RSSI signal received from the terminal is pre-processed by Kalman filtering before using RSSI for location solving.In this thesis,the beacon selection algorithm is designed according to the mean value of RSSI and the variance characteristics of each Bluetooth beacon,and the least squares(LS)method is used to estimate the unknown nodes.The thesis analyzes the impact of the number of beacon selection on the localization resul ts and verifies experimentally that the mean error of the LS algorithm based on beacon selection is reduced by 58% compared with the traditional LS positioning algorithm.Although the beacon selection improves the positioning accuracy,it cannot meet the demand of high-precision positioning.Therefore,this thesis introduces the weighted Gaussian Newton iteration(WGN)algorithm on top of the LS algorithm based on beacon selection,proposes a weighting design method based on the variance of distance and beacon RSSI values,takes the position calculated by the LS algorithm based on beacon selection as the initial value of positioning,and uses the Taylor series expands the objective function to optimize the positioning results by weakening the positioning error through circular iterations.A WGN algorithm with trajectory correction is also proposed,and the Kalman filter is used to correct the localization results of the WGN algorithm.The experiments show that the improved WGN positioning algorithm reduces the mean error value by 8% compared with the traditional WGN algorithm based on beacon selection under static point test,and reduces the mean error value by 30% compared wi th the beacon selection-based LS algorithm,and the WGN algorithm with trajectory correction reduces the mean error value by 2% and the maximum error by 25% compared with the improved WGN positioning algorithm.The improved WGN positioning algorithm under dynamic point test reduces the mean error value by 9% compared to the traditional WGN algorithm based on beacon selection,reduces the mean error value by18% compared to the beacon-based LS algorithm,reduces the mean error value by 8%compared to the improved WGN positioning algorithm with trajectory correction,and reduces the maximum error by 29%.