Research On Optimization Of Indoor Positioning Algorithm Based On WiFi Location Fingerprint

With the rapid popularization of mobile phone intelligent terminals and the maturity of wireless communication technology,people's demand for positioning services is increasing,and the application fields of wireless positioning technology are becoming more and more extensive,which involve transportation and logistics,industrial manufacturing,e-commerce,and public services.In recent years,there have emerged many indoor positioning technologies.Among them,WiFi signal has become the preferred technology because of the advantages of its wide wireless network coverage,no need for additional terminal equipment,low cost,and easy implementation and application.However,how to quickly and accurately build a location fingerprint database and ensure the positioning accuracy of the fingerprint database is an urgent problem to be solved in the process of large-scale application of the current WiFi indoor positioning technology.In view of the above problems,this thesis studies the preprocessing of the original collected data,the construction of the fingerprint database,and the optimization algorithm of the fingerprint database.The specific research contents are as follows:(1)In light of the problem that offline data collection is easily affected by environmental noise,this thesis proposes an improved Gaussian mixture filtering algorithm after analyzing the time-varying and distribution characteristics of WiFi signals.For the processing of original collected data,Gaussian filtering is first used,then the improved algorithm is used for data processing of small probability samples,and finally the mean filtering is used for processing,which can effectively reduce the effects of time-varying characteristics of the signal and the multipath propagation effects and ensure the stability and reliability of the original collected data.(2)In light of the problem that the construction of the location fingerprint database is time and labor-consuming,a method for building the fingerprint database based on the Gaussian process regression model is proposed.This method uses a small amount of sample data to train the Gaussian process regression model and constructs the fingerprint information of the entire positioning area.At the same time,this thesis also analyzes the influencing factors of the indoor environment and proposes a fingerprint database optimization algorithm that adds the propagation model of wall parameters,which not only reduces human and material resources consumption but also improves the positioning accuracy of the fingerprint database.(3)The improved algorithm and optimization algorithm proposed in this thesis are experimentally verified and analyzed.The experimental results show that the preprocessing method of the improved Gaussian mixture filtering algorithm can effectively improve the positioning accuracy of the fingerprint database.The fingerprint database constructed based on Gaussian process regression is compared with the original fingerprint database,and the positioning results are relatively close.The proposed optimization algorithm of the propagation model with wall parameters improves the average positioning errors of the original fingerprint database and GPR fingerprint database by 44.3% and 53.9% respectively.The positioning scheme of the GPR fingerprint database combined with the propagation model proposed in this thesis can quickly build the location fingerprint database and ensure the localization performance of the fingerprint database.