Research And Implementation Of GNSS/INS Loose Integrated Navigation Filter Algorithm

With the development of science and technology,navigation and positioning technology has advanced by leaps and bounds.Global Navigation Satellite Systems(GNSS)can provide all-weather,real-time high-precision positioning information,but the positioning accuracy is greatly interfered by the outside world.Inertial navigation system(Inertial Navigation System,INS)is a completely autonomous navigation technology that can independently output more detailed navigation information,but the navigation error will increase as the observation time increases.It can be seen that GNSS and INS are complementary in performance.At present,GNSS/INS integrated navigation has been widely used in production and life,providing high-precision and high-stability navigation information.The main research contents of the paper are as follows:(1)It elaborates the GASS composition structure,and analyzes the main functions of each,and discusses the main sources of errors in navigation and positioning,as well as the corresponding error reduction or removal methods.It deduces the process of pseudo-range single-point positioning,carrier phase positioning and differential positioning according to the ranging principle respectively,and analyzes and compares the advantages and disadvantages of the three different models.(2)It analyzes the platform and strapdown inertial navigation,expounds the INS positioning principle,discusses the commonly used inertial navigation coordinate system and the mutual conversion relationship between them,based on the working principle of gyroscope and accelerometer,Velocity and attitude deduced the mechanical orchestration equations,and finally analyzed the main sources of error in the INS positioning process.(3)It analyzes the GNSS/INS integrated navigation model classification,explains the specific working principles of different models,takes the GNSS/INS loose combined model as the research object,gives the state equation and observation equation,and discusses the standard Kalman filter and The two data fusion methods of adaptive Kalman filtering are analyzed and compared through the measured data.When there is a dynamic model,the accuracy of adaptive Kalman filtering is higher than that of standard Kalman filtering.(4)For the problem of multi-sensor observation information and low calculation efficiency,it proposes an algorithm for establishing adaptive Kalman filtering based on the Mahalanobis distance,using the predicted residual probability density to establish the Mahalanobis distance,and combining the navigation algorithm performs hypothesis testing.Based on the measured data,the standard Kalman filter,adaptive Kalman filter and improved adaptive Kalman filter GNSS/INS integrated navigation algorithm are compared and analyzed.