UAV Attitude Estimation Based on Improved Complementary Filtering Algorithm

Aiming at the problem that EKF and UKF algorithms based on Kalman filter algorithm framework require a large amount of computation and have high requirements on the processor chip, a UAV attitude estimation algorithm based on improved complementary filter algorithm is designed. The algorithm integrates the data of accelerometer, gyroscope and magnetometer with the gradient descent method and quaternion method. MATLAB was used for simulation verification. The simulation results show that the computational load of the complementary filtering algorithm is minimal, and it is easy to implement in engineering. It is especially suitable for low-cost microcontroller. Meanwhile, the error of the complementary filtering algorithm can also meet the flight requirements of system stability.