Bluetooth Low-Energy Networking And Positioning Technology Research

With the development of the mobile Internet era,intelligent mobile terminal devices and short-range wireless communication technologies have made rapid progress.The concept of the Internet of Things has made location-based services(LBS)a hot topic in recent years..The emergence of indoor location fingerprinting technology and the introduction of a new generation of Bluetooth Low-Energy(BLE),BLE technology with its low transmit power,high performance,mainstream smart devices fully support,so that indoor high-precision positioning The realization becomes possible.In this paper,aiming at the problem of indoor positioning,a low-power Bluetooth networking positioning system scheme and a Bluetooth WiFi fusion positioning system at the network side are proposed.The main research work is as follows:(1)This article builds an experimental hardware system based on the interest rate S1 iBeacon networking,uses the Android mobile phone as a mobile information acquisition platform,collects RSS of each Bluetooth node,stores the feature vector sample data into the database through weighted average processing,and builds an indoor positioning fingerprint database.Use-the positioning algorithm on the server to perform real-time positioning.(2)Simulate the RSS characteristics from the aspects of the spatial distribution characteristics of RSS and the impact of the environment,and optimize the algorithm for node deployment and algorithm improvement based on the shortage of fingerprint collection,and then perform real-time user data based on k-nearest neighbors.Fingerprint localization of k-Nearest Neighbor(k-NN).The experimental results show that the average error of the system is 1.66m.(3)The fingerprint localization based on the k-nearest neighbor algorithm has a great disadvantage for the tracking and positioning of the people's dynamic trajectory,and the RSS fingerprint matching has error.In this paper,Kalman filter algorithm and particle filter algorithm are introduced into the fingerprint location scheme,and the experimental simulation results show that Kalman filter and particle filter are used.The location accuracy of the algorithm is improved effectively,the average error of Kalman filtering system is 1.35M,and the average error of particle filter system is 0.76M.(4)Aiming at the advantages and disadvantages of Kalman filter and particle filter,a range matching fingerprint localization algorithm is proposed.In the beacon-intensive area,the positioning accuracy reaches sub-meter level 0.78m,and the calculation time reaches 0.26s,compared with Kalman filter.The accuracy increased by 42.2%,and the computation time was reduced by 93.56%compared to particle filtering.(5)This article refers to the fusion of WiFi positioning system into the filtering algorithm,compared with a simple Bluetooth positioning system,dual system fusion positioning accuracy increased by an average of 0.05m,the system performance increased by 6.41%,dual system fusion positioning through a large number of random path simulation,The experimental accuracy is up to 0.66m,which is 0.1m higher than the increase.(6)In this paper,the network-side positioning system is introduced,and the Bluetooth gateway and background server system are used to collect and position the location to be measured.The average accuracy of the network-side positioning system based on the Bluetooth system is 1.53m,which is 7.83%higher than that of the terminal side.It is based on Bluetooth.The average accuracy of the network-side positioning system with WiFi is 0.58m,which is 20.5%higher than that of the terminal side.