Research On Bluetooth And Dead Reckoning Based Indoor Positioning Algorithm

With the rapid development of information technology,especially the popularity of mobile Internet and mobile terminals like smart phones,Location Based Services(LBS)has brought great convenience to people's lives.The outdoor positioning technology represented by the global positioning system(GPS)cannot be used for indoor positioning since the signal is blocked,making the indoor positioning become a research hotspot.At this stage,there are many indoor positioning technologies,for instance infrared,ultrasonic,RFID,Bluetooth,UWB,ZigBee,computer vision,geomagnetic field,pedestrian dead reckoning and so on,however,because of the imbalances between the cost and accuracy,these technologies still have limitations,thus,it is crucial for the field of positioning and navigation to find a new indoor positioning technology which is low-cost,high-precision and real-time.At present,with the increasing amount of smart phones,which integrate with a lot of advanced hardware,such as Wi-Fi,Bluetooth and accelerometers,gyroscopes and so on,it is more convenient and feasible to develop the indoor positioning systems on smart phones.This paper utilizes the Bluetooth and inertial sensors embedded in smart phones as the method of data acquisition,focusing on the Bluetooth indoor positioning algorithm based on iBeacon and dead reckoning algorithm,then puts forward a fusion positioning algorithm based on the extended Kalman filter for Bluetooth and dead reckoning.Android smart phone as the hardware platform,together with the data serve,the three indoor positioning algorithms are implemented and tested in real scenes.The main tasks of this paper are described as following:(1)In this paper,the key part of Bluetooth indoor positioning,iBeacon is introduced,and then the Bluetooth signal propagation model is studied;in order to find out the relationship between the Received Signal Strength Indicator(RSSI)and the distance,this paper proposes a least squares propagation model training method based on piecewise fitting;in order to overcome the random jump of RSSI,the weighted sliding window filtering method is studied,then an improved trilateration indoor localization algorithm based on weighted averaging is proposed.(2)Analyzing the basic principle of dead reckoning and putting forward the implementation process of Pedestrian Dead Reckoning(PDR).Specifically,using finite state machine to determine whether the user is walking,using a step detection algorithm that combines acceleration amplitude exceeding-threshold detection and autocorrelation operation to carry out the detection of step number and frequency during the user's walking;using the nonlinear stride length model to estimate the user's step length.Due to there are a variety of mobile phone attitudes,this paper implements two algorithms to estimate the heading direction of pedestrian from azimuth angle and the principal component of horizontal acceleration by the user's gait features.(3)Comparing with the problems of Bluetooth localization and PDR positioning,and using the extended Kalman filter algorithm to fuse the two to improve the positioning accuracy.(4)In order to verify the feasibility and practicability of the algorithm,this paper designs and realizes the indoor positioning system based on Android platform and server,which includes three parts: Bluetooth iBeacon,Android client and server.The system uses Bluetooth and dead reckoning positioning fusion algorithm proposed in this paper,then comparing it with the single Bluetooth positioning and PDR positioning,the former's superiority is verified.