Research On Multi-source Position Information And Correction Technology For Vehicles

China is a major transportation country.With the rapid development of big data,artificial intelligence,machine vision,navigation and positioning technology,vehicle-road interactive intelligent transportation,autonomous driving,and intelligent travel have become the key directions for the current technological development of intelligent transportation systems.Autonomous driving that realizes vehicle-vehicle communication and vehicle-road interaction is an important sign of the rapid development of intelligent transportation.And the important prerequisite for realizing autonomous driving is to accurately determine the position and movement state of the moving carrier,provide reliable,safe,real-time and continuous positioning information for the vehicle,and provide a reliable basis for driving decision-making.The traditional single-sensor positioning method can no longer meet the current positioning accuracy requirements.Combining and complementing different sensors is currently the most widely used method.Based on the multi-sensor combined positioning method,this thesis mainly studies the two key technologies of information fusion and error correction,including sensor data denoising methods,inertial sensor error analysis,error modeling methods,adaptive fusion methods in combined positioning,and make corresponding improvements to these methods.The main research work and innovations of this thesis are as follows:Firstly,the basic principles of multi-source position information fusion technology are introduced,and the advantages and disadvantages of different levels and different fusion algorithms are analyzed in detail.At the same time,common navigation coordinate systems and their conversion relationships are introduced to establish an inertial sensor error model,The establishment of the error equations of the three-axis attitude,speed and position of the inertial navigation system,which lay the foundation for the following error correction technology and fusion method research.Secondly,taking the inertial sensor MEMS gyroscope as the research object,in order to solve the shortcomings of the traditional empirical mode decomposition method,propose an MEMS gyroscope random error modeling method based on improved EMD,and establish a new screening mechanism to divide the components into noise mode and mixed mode and signal mode,then,an ARMA model is established for the mixed mode and filtered,finally the signal is reconstructed to achieve a better error correction effect.Thirdly,taking the GPS/INS integrated navigation system as the research object,establish the integrated navigation system model,and introduce the algorithm flow of the standard Kalman filter and Sage-Husa adaptive filter method.Aiming at the measurement abnormalities in the filtering process,an adaptive fusion filtering combined with robust estimation method is proposed The fusion filtering method introduces an exponential decay adaptive factor to adjust the measurement noise covariance matrix,so that the system can achieve fault detection and isolation.Finally,for the sensor data denoising method,gyro random error modeling method,and robust adaptive fusion filtering method proposed in the thesis,set up simulation experiment,dual-axis turntable experiment and system on-board experiment verification and analysis to verify the effectiveness and superiority of the proposed method.It provides a certain reference value for the improvement of vehicle positioning accuracy and the improvement of system fault tolerance in engineering applications.