Research On Indoor Positioning Technology Based On Bluetooth 5.0 Beacon

In recent years,the rapid development of bluetooth technology has spawned a variety of applications of the Internet of things,among which the bluetooth-based indoor positioning technology has attracted more and more widespread attention due to its small size,easy realization and high universality.Bluetooth-based indoor positioning technology mostly by measuring the received signal strength indicator(RSSI)for ranging and positioning,but the instability of RSSI and other problems will affect the accuracy of positioning.In this paper,based on RSSI ranging positioning bluetooth-based technology,combined with the latest development trend of bluetooth,an indoor positioning system was designed and realized based on bluetooth 5.0 Beacon.In the measuring RSSI stage,the paper puts forward a kind of weighted KGMM hybrid filtering algorithm,which effectively makes data smoother.In the ranging stage,the paper proposes a ranging model based on the deep hidden multi-layer neural network,which can effectively reduce the ranging error.In the positioning stage,the paper improves the traditional three-point positioning algorithm and proposes an n-point positioning algorithm based on Bootstrap aggregating and k-Nearest Neighborhood(Bagging-k NN),which significantly improves the positioning accuracy in indoor environments.The main work of the paper is as follows:(1)The principle of Bluetooth Low Energy(BLE)5.0 and bluetooth Beacon technology is briefly introduced,and the commonly used signal attenuation model is discussed.On this basis,the existing Bluetooth-based indoor positioning technology is compared in detail.(2)The filtering algorithm in the measuring RSSI stage and the ranging model in the ranging stage are improved,and the RSSI-based ranging method based on fusion of weighted hybrid filtering and neural network is proposed.Firstly,for the fluctuation and instability of RSSI sampling value,various filtering processes are carried out for the data and experiment is carried out.An improved KGMM hybrid filtering algorithm is proposed.Then,a deep hidden multilayer neural network in machine learning algorithm is introduced to construct the nonlinear mapping relationship between RSSI and the distance from anchor node to signal receiver.Experimental results show that this method can effectively reduce indoor ranging error.(3)Aiming at the positioning algorithm in the positioning stage,an n-point positioning algorithm based on Bagging-k NN is proposed.Firstly,the traditional three-point positioning algorithm is studied and optimized for its shortcomings.An n-point positioning algorithm based on distance-weighted k-Nearest Neighbor(k NN)is proposed and analyzed experimentally.Then,an n-point positioning algorithm based on Bagging-k NN is proposed,which combines Bootstrap aggregating(Bagging)algorithm with k NN classification algorithm in machine learning algorithm,to improve the accuracy of positioning.The experimental results show that the average positioning error of the algorithm is about 7.35 cm,which has high stability and accuracy.(4)The indoor positioning system based on Beacon technology is built,and the feasibility of the proposed hybrid filtering algorithm,ranging method and positioning algorithm in the actual production environment was verified.The system includes bluetooth Beacon anchor node based on bluetooth 5.0 RF chip,mobile client of IOS system and host computer.Beacon anchor node uses Code Composer Studio(CCS)integrated development environment to realize the function of Beacon broadcast.The mobile client uses Xcode to develop the IOS client APP,which realizes detection of bluetooth signal and real-time collection and processing of RSSI signal.The host computer mainly completes filtering the data,implementing the ranging and positioning algorithm module.The system can provide more accurate positioning function and display the positioning results,which can meet the needs of most indoor positioning scenes.