Kalman Filtering Algorithm for Integrated Navigation System in Unmanned Aerial Vehicle

Through the complementarity of the satellite navigation positioning system (GNSS) and the inertial navigation system (INS), the combination of GNSS/INS can make up for the shortcomings of a single system that is difficult to improve, thereby greatly improving the accuracy of the integrated navigation system. In this paper, the transformation relationship between the carrier coordinate system and the navigation coordinate system is deduced, and the INS position and velocity measurement methods are derived. Based on the theory of GNSS/INS loose combined system and Kalman combined filter, the system equation and measurement equation of combined filter Kalman are studied in depth. The principle of discrete Kalman filter is researched, and five basic equations of Kalman filter are given. Then the speed and position model of Kalman filter of GNSS/INS loose combined navigation is derived in detail, and according to the speed and position model perform semi-physical simulation experiments. The experimental results show that the inertial sensor can provide navigation information during the GNSS receiving signal gap, and the position and speed information of the GNSS can also correct the navigation information of the inertial sensor.