Research And Implementation Of Indoor Positioning Technology Based On Bluetooth 4.0 And Location Fingerprint

With the rapid development of mobile Internet technology,demand of Location Based Service(Location Based Service,LBS)is also increasing.Until now,a series of outdoor positioning technology based on GPS has matured,it has been widely applied in many outdoor positioning applications,but there are some blind spots when it is used in indoor environment,so that it can not meet current location needs.At the same time,with the emergence of a large number of indoor applications,the acquisition of indoor location is particularly important,so people gradually turned their eyes to the development of indoor positioning technology to address the existing difficulties and achieve new breakthroughs.The emergence of Bluetooth Low Energy(BLE)has created a new direction for indoor positioning technology,the BLE has many advantages such as easy to deploy,high precision,less susceptible to environment,low power,low cost,so that it is favored in indoor positioning field.Especially in 2013,the Apple Inc launched iBeacon near-field based on BLE,which lead to it being widely used in various indoor environment.The BLE has become a hot topic in indoor positioning field gadually.On the basis of existing research of indoor positioning technology and BLE technology,we uses K-means fingerprint algorithm based on Received Signal Strength Indicator(RSSI)to achive quick positioning and following,designed a series of optimization in the offline stage and online stage,and proposed a finite region K-means indoor positioning algorithm based on proximity,the algorithm acchived high precision and high timeliness in experiment.In this paper,first of all,we taking into account that the actual indoor environment due to human,objects,walls and other factors will lead to signal reflection,refraction,diffraction,thus affecting the establishment of fingerprint database in offline phase.So we studythe distribution characterstics of Bluetooth 4.0 location fingerprint,find the relationship between the signal strength and the obstacle,the propagation distance and the traffic flow.On this foundation,we proposed the optimization fingerprint database to gurantee the validity of the fingerprint map,and the positioning accuracy in the subsequent online positioning stage.Furthermore,in the online positioning stage,we makes a deep analysis of the existing positioning algorithm,and points out its limitations.On this basis,a finite region K-means algorithm has been proposed,it optimized the K-means clustering algorithm by using the proximity which can reduce the space of matching search,then reduce the positioning time.The experimental results show that the optimized K-means positioning algorithm has a significant improvement in performance and time compared with the traditional algorithm.Finally,this paper applied the optimization scheme in practice,and achieved an intelligent guide application based on positioning system,then depolied it into the actual museum environment as a test.The test results show that the whole performances and user experiences are outstanding,it has a positive reference significance for practical commercial application.