Adaptive extended Kalman filter based on SOA algorithm for UAV attitude solution

Abstract

Aiming at this problem of the inertial measurement unit has high noise, low precision and large error in traditional attitude calculation methods, an Extended Adaptive Kalman Filter algorithm was proposed to optimize attitude data. The algorithm first builds a state equation model based on sensors such as gyroscope, accelerometer, and magnetometer, with gyroscope data as prediction data, accelerometer and magnetometer measurement values as observation data, and performs error compensation and filtering on the collected raw data. The Seagull Optimization algorithm (SOA) is used to optimize the process noise covariance and measurement noise covariance of the Extended Kalman Filter. Finally, a high-precision aircraft attitude estimation is obtained after Adaptive Extended Kalman Filter algorithm (AEKF) filtering. Both static and dynamic experiments are carried out on the flight experiment platform based on INS-DH-OEM inertial navigation system. Comparing and analyzing the filter effect of the traditional extended Kalman filter algorithm and the adaptive extended Kalman filter algorithm proposed in the paper. Through the experimental results, the algorithm proposed in this paper can suppress the drift of attitude angle, filter out noise and accurately track the attitude change. In the static test, the accuracy of the three attitude angles can be controlled within 0.1°. Compared with the traditional EKF algorithm, the proposed algorithm has better stability and higher accuracy. In the dynamic experiment, the gyroscope has good dynamic performance but with the passage of time, it will produce integral drift, and in this paper, the accelerometer is used to carry out a real-time drift correction of the gyroscope. The roll Angle error and pitch Angle error of this algorithm are within 0.5°, which is higher than that of the traditional EKF algorithm.