The Research Of WIFI Indoor Location Algorithm Based On Location Fingerprint Recognition

With the explosive growth in the number of smartphones and the growth of the mobile Internet,there is an increasing demand for providing accurate location-based services in indoor environments.As a ubiquitous network infrastructure under indoor environment,WIFI has become a research hotspot in indoor positioning technology due to its high positioning accuracy,low positioning cost and strong anti-interference.This thesis systematically studies the RSS-based WLAN fingerprint location technology.It analyzes the key factors affectin g the accuracy of final positioning and the time complexity of positioning in the typical fingerprint database two-step positioning system.The contributions of this thesis are as follows: analyzing the characteristics of WIFI signal in indoor specific environment;It discusses the effect of logarithmic path loss model on RSSI signal fitting,and analyzes the parameter optimization problem of path model under different floor conditions.In the off-line analysis stage,a comprehensive analysis of the WIFI signal is made,focusing on various factors that may cause the positioning accuracy to be reduced.The experimental results show the anisotropy of the terminal during the acquisition phase.The influence of the orientation of the acquisition terminal on the signal strength of the WIFI is also analyzed and compared.Through data analysis,it points out the phenomenon of signal disappearing which is ubiquitous during the construction of fingerprint database,and proposes the pretreatment of using this method to approach the average interpolation to reduce the positioning error caused by inaccurate data of fingerprint database.Finally,the paper improves the traditional K-value weighted WKNN algorithm to avoid the WKNN using a fixed K value in the positioning phase.At the same time,in order to reduce the time complexity of the whole positioning system,a two-layer improved algorithm based on Kmeans and EWKNN is proposed.Experiments show that the whole system can effectively improve the positioning accuracy and reduce the time complexity of locating large area.