Study On WLAN Based Indoor Localization Algorithm

With the development of mobile communication and location technology,WLAN based indoor location has attracted more and more attention,and shown great energy in pedestrian navigation,emergency and medical care areas.However,existing WLAN based indoor localizationcannot satisfy people's needs in location accuracy,real time and flexibility performance,whick limits its large scale application and popularization.Through the review and analysis in the research status of indoor localization technology,the existing WLAN based indoor localization have following serval problems: firstly,with affected by multipath effect,people absorption and signal interference in complex indoor environments,WLAN signal strength suffers from fluctuation and jump,which seriously affects the location accuracy and stability.Secondly,the existing matching based location algorithm has a large calculation quantity,which easily lead to loacation delay when running in mobile terminal.Lastly,the existing researchs mainly focus on two dimensions positioning,and seldom involve in multi floor positioning problem.The thesis makes a deep and systematic study in WLAN based indoor localization.Aiming at existing problems in location technology,the thsis proposes corresponding solutions.The research contents are as follows:Proposing an Importance Weighted Density Function(IWDF)based fuzzy clustering algorithm to cluster the WLAN signal strength fingerprints databased,which helps accelerate location speed.With the helps of clustering procedure,the amount of calculation in location phase will be reduced,meanwhile it also helps reduce the location error caused by mismatching and improve the location accuracy.The Fuzzy C Means(FCM)clustering algorithm uses the fuzzy membership to describe clustering results,which helps reduce the probability of mismatching and the location error caused by mismatching.The choice of initial cluster center is important for FCM clustering algorithm,and diffierent initial cluster center will lead to different clustering results.The thesis uses IWDF to calculate the initial cluster center,which helps improve the clustering results and reduce the time consumption of clustering.As a reasonable clutering initial algorithm,the IWDF considers the difference of importance and density among location data,the data with higher importance weights and dense is chosen to be the initial cluster center,which avoids the clustering results fall into singular values and local optima.In order to improve the location accuracy of WLAN localization algorithm,the thesis proposes a Cooperative Particle Swarm Optimization(CPSO)based Artifical Neural Network(ANN).With the signal strength data collect in offline phase,the CPSO algorithm trains the structure and parameter of ANN at the same time.During the online location phase,the location results will be estimated once the online collected signal strength data is inputing to trained ANN model.Compared to the traditional fingerprinting algorithm,the CPSO algorithm has less computional works during the online location phase,and has a fast location speed.Meanwhile,due to the nonlinear generalization ability of ANN,the CPSO-ANN localization system has a high location accuracy.Aiming at the large location error caused by WLAN localization jumping,the thesis proposes an Adaptive Particle Filter(APF)based WLAN-Pedestrain Dead Reckoning(PDR)combined localization algorithm.The WLAN-PDR localization algorithm combines the advantages of two respective location algorithm,to get a continuous,stable and accurate location results.Meanwhile,aiming at improving the location accuracy in mobilephone,the thesis improves the PDR algorithm in gait detection,step length estimation and heading calibration.In order to solve the multi floor location problems,the thesis proposes a pedestrian multi floor recognition method.The method detects the floor change by measuring the change of environment air pressure,and uses the FCM clustering results to helping determine the specify floor number.The method ability of resisting disturbance is improved by using the fuzzy membership.Even in areas with little WLAN signal features,the method can achieve a well floor recognition accuracy.