Research On Vehicle Attitude And Navigation System Based On Improved MARG Sensor

Vehicle navigation and positioning systems have been integrated into people's daily lives,and researchers are still making efforts to improve the accuracy and to reduce the cost of vehicle navigation.The single inertial navigation system and the satellite navigation and positioning system cannot achieve normal navigation in many cases.Therefore,the attitude and heading reference system and the combined navigation system are the key to the vehicle-mounted navigation and positioning system.Vehicle attitude information in the attitude and heading reference system can be used in vehicle safety detection and integrated navigation systems.In the integrated navigation system,many kinds of sensor information and system information are integrated.All kinds of information can effectively improve the navigation accuracy through complementary ways.The main research work of this paper is as follows:Firstly,it summarizes the research status of inertial navigation system and attitude measurement system at home and abroad,and basic theoretical knowledge.The speed,attitude,position update algorithm and error state model are derived.It also introduces the application of Kalman filter in integrated navigation,deduces the time updating and measurement updating formulas of Kalman filter,and defines common coordinate systems and various calculation constants.Secondly,in order to improve the accuracy of the attitude and heading reference system of the moving vehicle,a low-cost MARG(magnetometer,gyroscope and accelerometer)sensor assisted by the odometer(OD)is designed with an algorithm that can accurately estimate the attitude.Using the twelve-parameter ellipsoid fitting method combined with neural network,the magnetic field measurement value is compensated to solve the non-orthogonal error and carrier magnetic field interference.Using the odometer.MARG sensor and low-pass filter,the harmful noise in the measured value of the accelerometer is removed to obtain an accurate acceleration value.An adaptive Kalman filter(AKF)algorithm is designed.By adjusting the noise matrix,the filter gain can be adjusted adaptively under different operating conditions(stationary,normal driving,fast acceleration,fast deceleration,turning).The experimental results show that the average errors of pitch,roll and yaw angle between the AKF algorithm and the reference values are 0.45°,0.610 and 0.85°,which verifies that the attitude algorithm proposed in this paper can estimate the vehicle attitude information better.Finally,based on the inertial navigation system and the attitude reference system,the SINS/OD/MAG integrated navigation system is designed.In order to achieve multi-sensor information fusion,an adaptive federated Kalman filter(AFKF)algorithm is designed,which includes SINS/OD and SINS/MAG two subsystems.In the actual operation of the vehicle,information is adaptively distributed according to the accuracy of the subsystems,so that Global filter has self-adaptability.The design experiment is compared with the SINS/OD navigation system,which proves that the positioning accuracy of the SIN S/OD/MAG integrated navigation system based on the AFKF algorithm is relatively high.The east and north directions between the AFKF algorithm and the reference value output.The mean square error of speed is 0.2139m/s and 0.2219m/s respectively,and the mean square error of east and north position is 3.1325m and 3.2437m respectively.An experiment of abnormal vehicle operating conditions was designed,and a centralized Kalman filter(CKF)and AFKF algorithm were built for comparative analysis.The experimental results show that when a certain sensor information undergoes a large mutation,the AFKF algorithm can effectively suppress the error divergence and has a high Fault tolerance and high reliability.