Design And Implementation Of Multi-Source Fusion Ubiquitous High Precision Positioning Terminal

In recent years,with the rapid development of intelligent technology,there are more and more large-scale exhibitions in venues,and the facilities in tourist attractions are becoming more and more intelligent.Therefore,intelligent guided robots have become a development trend.For example,in the Beijing Winter Olympics,many smart tour robots will be exhibited to provide guided tour and explanation services.How to accurately obtain the current position of the guiding robot and continuously and accurately locate it during the guidance process has become a research hotspot.Traditional outdoor satellite navigation and positioning methods are only applicable to specific scenario.If an emergency situation occurs and people need to be guided to safe areas such as tunnels or indoors,the robot cannot be positioned due to the lack of satellite signals;and the Bluetooth or Visual Positioning of the robot in an indoor environment will also be affected by various situations.Such external conditions greatly reduce the positioning accuracy;at the same time,the continuity of indoor and outdoor switching is also an urgent problem to be solved.Therefore,in order to continuously and accurately locate the position of the robot during the guidance,this thesis designs a multi-source fusion ubiquitous high-precision positioning terminal,which can be installed on the intelligent robot to assist coherent positioning in the ubiquitous scene.Based on the characteristics of satellite and Bluetooth signals,the terminal can recognize the current motion scene,and design different combined positioning algorithms according to different scenes,so that the robot can continuously obtain high-precision positioning during indoor and outdoor movement.First of all,for the problem that different scenes are difficult to distinguish,this thesis proposes a scene-recognition algorithm based on a fully connected neural network.Using features such as the number of satellites,Elevation mask angle,Signal-to-Noise Ratio,and Bluetooth signals to construct scene classification feature data,and input the fully connected neural network for scene classification.It can effectively distinguish indoor scenes,outdoor scenes,and indoor and outdoor fuzzy zone scenes.Secondly,in view of the problem that the positioning in the indoor scene is greatly affected by the environment and hardware equipment,this thesis proposes a dynamic fusion positioning algorithm of Bluetooth and inertial navigation based on the environment adaptive Extended Kalman Filter.The algorithm uses multi-stage filtering to correct the Bluetooth RSSI(Received Signal Strength Indicator)data,and introduces a fluctuation coefficient to eliminate the volatility of the Bluetooth beacon for location fingerprint positioning.Then,the algorithm combines the results of the Bluetooth positioning with the results of inertial navigation and adds the Bluetooth beacon path loss exponent as an observation noise.Based on this,the observation noise is input to the Extended Kalman Filter for error correction to achieve high-precision and stable indoor positioning.In the outdoor environment,aiming at the short-term lack of satellite signals in some scenarios,this thesis integrates satellite system and inertial navigation system.The system installation space error and time asynchrony error are added as observation noise to the Extended Kalman Filter to optimize the state space model.In this way,high-precision positioning can be achieved in outdoor short-term weak satellite signal scenarios.Then,in order to solve the problem of the robots cannot move coherently and continuously between different scenes,this thesis proposes a position aggregation algorithm based on probability density function fitting in the semi-open fuzzy zone between indoor and outdoor scenes.The error distributions of Bluetooth,inertial navigation and RTK(Real Time Kinematic)are fitted using the Gamma function.Based on that,this thesis proposes a time distance weighting method to aggregate them into a single position estimate,which realizes a smooth transition between indoor and outdoor.Finally,based on the above-mentioned positioning algorithm,this thesis designs and implements a multi-source fusion high-precision positioning terminal.i.MX8MMini is selected as the core processor,NEOM8T is selected as the GNSS(Global Navigation Satellite System)receiver,and OPENIMU300 is selected as the inertial navigation sensor.Combined with the power module,communication module,and interface expansion module,a 4-layer PCB(Printed Circuit Board)hardware circuit board is designed.At the same time,a personalized Bluetooth beacon receiver is designed,and the terminal circuit board is connected through the USB interface to collect the Bluetooth signals required for positioning.The performance of the terminal is verified through actual indoor and outdoor environment tests.Field tests show that the terminal designed and implemented in this thesis can achieve an average positioning accuracy of less than one meter in the experimental environment,achieved the goal of ubiquitous high-precision positioning.