Research On Integrated Navigation System Based On GNSS/INS

With the development of car navigation technology,a single car navigation system is no longer sufficient for the needs of automobiles.In order to pursue high-precision positioning,multiple systems are usually required to perform coordinated positioning.In the process of car driving,when the observation conditions are good,GNSS can be used to obtain the navigation information of the vehicle for positioning.When the vehicle is driving in tunnels,underground garages and other scenes,navigation and positioning simply rely on GNSS cannot be carried out due to signal occlusion.After the INS is initialized,it can use its own sensors to obtain the angular velocity and acceleration information relative to the outside,and the navigation information of the carrier can be obtained without interference from external conditions.However,the errors of the INS will accumulate over time,so GNSS and INS have very Good complementarity.This article is oriented to the application of vehicle integrated navigation.It makes full use of the gear and odometer information output from the CAN interface of the vehicle to establish a mathematical model of GNSS/INS vehicle integrated navigation.The problem of difference and so on establishes an adaptive federated Kalman filter model that can effectively resist gross errors.The main research contents of this article include:(1)The method for setting the variance of the measurement information in the dynamic kinematics constraint is studied.Aiming at the actual situation that the lateral and vertical speeds of the vehicle are different in different road conditions,a method of adaptively adjusting the variance of the dynamic kinematics constraint based on the accelerometer variance information is proposed.According to the method,a set of adaptive Kalman filter model with additional constraint information is established.The performance of the adaptive Kalman filter model is verified.The experimental results show that the accuracy of the system model proposed in this paper is better than that of the traditional Kalman filter model when the GNSS signal is interrupted.Therefore,this scheme is more suitable for vehicle integrated navigation applications.(2)Aiming at the problem of abnormal observations such as gross errors in the vehicle navigation system during the calculation process,this paper uses the measurement innovation as an adjustment factor to adaptively adjust the error distribution of the federated Kalman filter,combined with dynamics Adjust the mathematical model of dynamic kinematics constraint variance,and establish an adaptive federated Kalman filter model suitable for in-vehicle integrated navigation applications,which effectively improves the reliability and navigation accuracy of integrated navigation.On the basis of the above,the correctness and effectiveness of the model are verified through actual sports car data.During the test,mature commercial high-precision optical fiber inertial navigation is also used as a reference benchmark.In the data comparison,four pieces of data were randomly selected to artificially add a position error of 15 m to the GNSS observation information to simulate the observation anomaly.The experimental results show that the position,speed and attitude of the system model proposed in this paper are better than the accuracy of the traditional scheme,thus verifying the performance of the system model in this paper to resist gross errors.Finally,the navigation performance of the adaptive federated Kalman filter method proposed in this paper is evaluated in a typical urban scene.The experimental results show that when the observation information is good and there is no interruption,the position accuracy of the system can reach centimeter level,the speed accuracy can reach 0.1m/s(1?),and the attitude error accuracy is better than 0.2°(1?),when the GNSS signal is interrupted,The typical index of the position navigation information of the system is 0.3% D(D is the distance traveled by the vehicle after the GNSS signal is interrupted),which verifies the feasibility of the model applied to the integrated navigation of the vehicle.