The Application Research Of Volumetric Kalman Filter Algorithm In SINS/BDS Integrated Navigation

With the development of modern science and technology,strapdown inertial navigation systems(SINS)has been widely used in aerospace,aviation,navigation and other fields,SINS is a fully autonomous navigation system with good concealment,strong autonomy,and high positioning accuracy in short distance.While with the accumulation of time,it leads to increased navigation error.In recent years,the Beidou navigation Satellite system(BDS)has developed rapidly.BDS has all-weather,high-precision,continuous positioning and other characteristics,but its signal is vulnerable to external interference.Therefore,SINS and BDS are combined.This dissertation is based on the SINS/BDS integrated navigation.The problem of large misalignment angles in the initial alignment of the SINS in the Cubature Kalman filter(CKF)and the strong nonlinearity of the combined system result in negative covariance matrix problems.The main work of this paper can be summarized as follows:(1)Aiming at the problem of selecting the initial value of the standard CKF in initial alignment of SINS with large misalignment angles,an improved adaptive CKF algorithm is proposed.In order to solve the problem of initial alignment under the condition of large misalignment angle of SINS,a large misalignment angle error model based on Euler angles is established.The standard CKF algorithm is used as the theoretical framework.The adaptive factor is introduced and the adaptive CKF algorithm is used to adjust the amount in real time.The weight of the measurement and status information in the filtering process.The improved adaptive CKF algorithm can effectively suppress the divergence of filtering,improve the alignment accuracy,as well as the stability and self-adaptability of the filter.(2)In the SINS/BDS integrated navigation algorithm,the accuracy of the filtering algorithm is degraded or even divergence.In order to solve the problem,The singular value decomposition method is combined with the Cubature Kalman filter algorithm,and the strong tracking theory is introduced.An improved strong tracking SVD-CKF is proposed.algorithm.In order to improve the stability of numericalcalculations,the algorithm uses singular value decomposition instead of Cholesky decomposition in simplified Cubature Kalman filtering.The algorithm introduces a strong tracking filter theoretical framework and online correction of the prediction error covariance matrix through the fading factor.When the system model is indefinite or the system undergoes large abrupt changes,the robustness of the system can be improved.The simulation results verify the effectiveness of the improved algorithm.(3)In combination with the SINS/BDS integrated navigation system hardware,the loose combination method of the SINS/BDS integrated navigation system is designed,and the feasibility of the SINS/BDS loose integrated navigation system is verified through the outdoor car test.In this paper,the large misalignment initial alignment and the improved strong tracking SVD-CKF algorithm studied in this dissertation improve the accuracy and robustness of the integrated navigation system.