Research On Adaptive Filtering Algorithm Of SINS/GPS Integrated Navigation

This paper takes tightly coupled navigation model of strapdown inertial navigation system and global position system as research background,focuses on data fusion theory and algorithm of SINS/GPS tightly coupled navigation system.This paper proposes an adaptive fading filtering algorithm based on innovation covariance and introduces a Kalman filter algorithm based on squared sum increment of weighted least squares residuals,which are both applied to the SINS/GPS tightly coupled navigation system.The main contents and innovations of the paper are summarized as follows: 1.This paper deeply studies loosely coupled mode and tightly coupled mode of SINS/GPS integrated navigation system.Through data calculation and analysis of the simulation experiment,the characteristics and differences of these two kinds of coupled modes are illustrated,as well as the advantages of choosing the tightly coupled mode.2.This paper deeply analyzes the standard Kalman filter algorithm,narrating the theoretical derivation of this algorithm,and pointing out the limitation of Kalman filter algorithm.In order to overcome this problem,this paper introduces nonlinear extended Kalman filter algorithm and unscented Kalman filter algorithm.3.This paper studies basic theory of fading filtering algorithm.The influence of the position of the adaptive fading factor on the filtering solution is analyzed.Then the paper introduces the derivation process of classical fading filtering algorithm and the method to calculate the fading factor.We proposes an adaptive fading filtering algorithm based on innovation covariance.4.We establish a simulation experiment platform of SINS/GPS tightly coupled navigation system,meanwhile standard Kalman filter,fading filter,and adaptive fading filter based on innovation covariance are successfully applied to the SINS/GPS tightly coupled navigation simulation system.Simulation experiment results show that the fading factor constructed in the adaptive fading filter based on innovation covariance can effectively suppress the bad influence of errors in the dynamic simulation motion,improve the filtering accuracy,and obtain better convergence results.5.Through actual vehicle experiments,we measured and analyzed the ability of three different kinds of filtering algorithms.The paper studies the influence of navigation results by using the fading factor of the adaptive fading filtering algorithm based on innovation covariance.The results demonstrate that fading factor can well suppress the divergence of the filter,and reduce the adverse effects of system errors changing in most cases.6.This paper introduces a Kalman filter algorithm based on the squared sum increment of weighted least squares residuals.The specific derivation process of the algorithm is described in detail,and characteristics and effects of the algorithm are analyzed and discussed.The feasibility and effectiveness of the algorithm in SINS/GPS integrated navigation system are verified by actual static experiments.Compared with the Kalman filter algorithm,this algorithm is better because the accuracy is obviously improved and the reliability is also more significant.