Design Of UAV Integrated Navigation Information Fusion Methods Based On GPS/INS

The Unmanned Aerial Vehicle(UAV)has the advantages of small size,light weight and easy portability and is widely used in military and civilian fields.As the most important component of the UAV,the navigation system is related to various safety issues during the flight of the UAV and is the foundation of achieving a stable flight of the UAV.The combination of the Global Positioning System(GPS)and the Inertial Navigation System(INS)has the characteristic of advantage complementary and can fully take advantages of both the subsystems,and improve the accuracy and reliability of the navigation system.However,the integrated navigation system is too dependent on GPS signals in the navigation process.When GPS signals are blocked or interfered,GPS cannot achieve the error of the integrated navigation system,and it is difficult to limit the inertial navigation system error accumulation over time.So,the inertial navigation systems diverge.Therefore,this article focuses on this issue and studies how to improve the accuracy and reliability of the navigation system when GPS is abnormal.The main work is summarized as follows:(1)The basic theory of GPS/INS integrated navigation is described.The error characteristics of the INS and the GPS are studied,and the error models of the inertial navigation system are established.At the same time,the algorithms for solving the information(posture,position and velocity)of inertial navigation system are studied,and the GPS/INS integrated navigation system is set up.(2)A GPS/INS integrated navigation information fusion method based on Kalman filter is designed.Firstly,Kalman filter is used to calculate the state equations and measurement equations of the integrated navigation system.Then,the combined navigation system estimated and calibrated,and the loose combination mode is used to handle the date of attitude,position and velocity information provided by the global positioning system and inertial navigation system.Finally,the system is simulated.The simulation results show that the Kalman filter algorithm's estimation of position,velocity and attitude angle is always in a relatively stable state,and the accuracy of navigation is also at an acceptable level.(3)A BP neural network-assisted GPS/INS integrated navigation informationfusion method based on Kalman filtering is proposed.The theoretical knowledge and characteristics of artificial neural network are studied.The BP neural network algorithm is studied emphatically.The BP neural network algorithm is introduced into the integrated navigation system to solve the problem of information divergence in the integrated navigation system when the GPS signal is abnormal.When the GPS signal is normal,the integrated navigation is filtered and the real-time flight data is used to train the BP network model.When the GPS signal is abnormal,the Kalman filter's measured value is predicted based on the previously trained BP network model.The Kalman filter is updated and the Kalman filter is used to filter and estimate the inertial navigation data,so as to improve the reliability of the integrated navigation system under the abnormal GPS signal condition.The simulation results show that the algorithm can maintain a low bias in a short time,which has a certain inhibitory effect on the divergence of information in the integrated navigation system,and can effectively improve the reliability of the system.