Research On Adaptive Filtering Algorithm For High-precision Vehicular Integrated Navigation

In recent years,unmanned driving technology has developed vigorously and is widely used in logistics,shared travel,sanitation,military and other fields.In order to ensure the safe and reliable driving of unmanned vehicles,the research of high-precision vehicle integrated navigation system has always been the key of unmanned driving technology.In airport logistics transportation,unmanned vehicles are required to have accurate navigation information within the specified route.However,due to the influence of the environment such as covered bridges and tunnels in the airport,the on-board sensors are vulnerable to interference,which leads to the inability to meet the needs of high-precision navigation.How to combine the vehicle sensors reasonably,improve the robustness and fault tolerance of the integrated navigation system,and provide continuous and stable high-precision navigation parameters for unmanned logistics vehicles is the main research content of this paper.The specific research contents are as follows:Firstly,the navigation principles of Strapdown Inertial Navigation System(SINS),Global Navigation and Positioning System(GPS)and Odometer(OD)are introduced respectively,their error models are deduced,the advantages and disadvantages of the three systems are compared and analyzed,the necessity of their combination is expounded,and the overall scheme of the system is designed,which provides a theoretical basis for the subsequent algorithm simulation.Secondly,the working principles of Sage-Husa filter and variational Bayesian adaptive filter are introduced,and the advantages and disadvantages of the two adaptive filtering algorithms are analyzed through research and simulation.Simulation results show that,compared with Sage-Husa filter,the variational Bayesian adaptive filter has better tracking ability and higher filtering accuracy for changing measurement noise R.To solve the problem that the variational Bayesian adaptive filtering algorithm can’t accurately estimate the time-varying system noise Q and measurement noise R at the same time,an improved variational Bayesian adaptive filtering algorithm based on inverse Wishart prior distribution is proposed,which estimates the measurement noise R and indirectly estimates the system noise Q by estimating the one-step prediction covariance P,thus reducing the computational complexity of the algorithm and improving the real-time performance of the system.The effectiveness of the improved adaptive filtering algorithm is verified by simulation and driving test experiment.Finally,in order to improve the accuracy of integrated navigation under the condition of GPS accuracy degradation or even outage,SINS/GPS/OD is fused with multi-source information.In view of the error characteristics of OD,a filtering algorithm for OD online calibration is designed,which estimates and compensates the calibration factor error,installation angle error and lever arm error of OD.The simulation results show that the position accuracy is close to 1‰D,where d is the driving distance.At the same time,in order to solve the problem that the navigation accuracy of the system is reduced due to the reduction of sensor measurement accuracy in complex environment,an adaptive federated Kalman filter based on observability analysis is proposed,a simple and effective observability calculation method with clear physical meaning is derived,and the observability degree of navigation errors of SINS/GPS and SINS/OD subsystems is calculated respectively,according to which the information distribution factor of federated filter is adjusted adaptively.The proposed algorithm overcomes the problem that the system accuracy drops sharply due to the degradation of subsystem performance,and effectively prevents the global information from being polluted.Finally,the effectiveness of the proposed algorithm is proved by simulation and driving test experiment.