Research On WiFi Indoor Location Method Based On GMM

Since the beginning of the 21 st century,Internet information technology is developing at an alarming speed.There is a growing demand for location-based services.With the deployment and improvement of GPS and Beidou system,outdoor location service can meet people's basic travel positioning needs.However,the GPS and Beidou systems are still unable to achieve more accurate positioning in the face of the signal weakening and multipath effects caused by complex indoor environment.WiFi indoor positioning technology has become the preferred indoor positioning technology because of its high positioning accuracy,high stability and low cost.In this paper,EM algorithm and K-means++ algorithm are used to construct fingerprint database.Naive Bayesian formula and LVQ algorithm are used to complete the matching technology of the points to be located.The main work is as follows:(1)The influence of initial parameters on the iteration times and results of EM algorithm is analyzed.K-means++ algorithm is proposed to initialize the model parameters.The simulation results show that the iteration times of EM algorithm and the cumulative error of the Gauss mixture distribution model are optimized after initializing the parameters with K-means++ algorithm.The computation amount of EM algorithm is reduced by 22.5%~25%.(2)Fingerprint database generated by single-Gaussian distribution model,common EM algorithm and EM+K-means++ algorithm combined with Naive Bayesian formula is used for location simulation.The simulation results show that the smaller the cumulative error of probability model of fingerprint database is,the higher the location accuracy is.Among them,the fingerprint database generated by EM+K-means++ algorithm has the highest location accuracy,and the sampling interval is 2 meters.In this case,the average positioning error is 1.56 meters,which is3.2%~7.3% higher than the common EM algorithm.(3)By analyzing the time complexity of Naive Bayesian method,it is found that the operation of Naive Bayesian method is large.The LVQ algorithm is proposed to divide the location area into several small areas,extract the prototype vectors of sampling data from each area,match the measured data with the prototype vectors,locate the location point to the small area where it is most likely to be located,and then use it in this area.Naive Bayesian method for accurate positioning.Through simulation experiments,it is found that the amount of matching operation is reduced by56%~68% with the original positioning accuracy guaranteed by using LVQ algorithm.The work of this paper can provide theoretical and practical support for indoor location based on WiFi fingerprint database in constructing fingerprint database based on Gauss mixture distribution model and using Naive Bayesian method for location matching.