Research on High-precision Algorithm for Flight Attitude Evaluation of Quadrotor Aircraft

With the rapid development of UAV technology, quadrotor aircraft has shown wide application potential in many fields. In this paper, a high-precision algorithm based on multi-sensor data fusion and optimized extended Kalman filter (EKF) technology is designed and implemented for the flight attitude evaluation of quadrotor aircraft. By fusing the data of IMU (Inertial Measurement Unit), GPS (Global Positioning System) and magnetometer, the algorithm can effectively resist noise interference and reduce sensor errors, and realize real-time and high-precision estimation of aircraft attitude angle and position. The experimental results show that the algorithm performs well in a variety of flight trajectories, which is basically consistent with the real trajectory, especially in complex movements such as hovering and diving, and can still maintain high accuracy and robustness. Compared with the commonly used Mahony and Madgwick algorithms, the algorithm proposed in this study has obvious advantages in attitude estimation error fluctuation range, error stability and error average. This not only improves the maneuverability and safety of the aircraft, but also provides strong support for the autonomous flight and precise control of the UAV. This study provides an important reference for the field of flight attitude evaluation of quadrotor aircraft.