An improved FastEuler-DLKF small-UAV AHRS algorithm
This work addresses the need for reliable flight systems in small UAVs for near-ground navigation, representing an incremental improvement in sensor fusion methods.
The paper tackled the problem of accurate attitude estimation for small UAVs using low-cost sensors by proposing an improved FastEuler Double-Layer Kalman Filter algorithm, which experimental comparisons showed provides more accurate and reliable attitude information than a Complementary Filter.
The accurate Attitude Heading Reference System(AHRS) is an important apart of the UAV reliable flight system. Aiming at the application scenarios of near ground navigation of small-UAV, this paper establishes a loose couple error model of the gyroscope/accelerometer/magnetometer, and presents an improved FastEuler Double-Layer Kalman Filter algorithm. Using low-cost devices which include MEMS Inertial Measurement Units(IMU) and magnetometers, this paper constructs the AHRS hardware and software systems of UAV, and designs the offline and real-time verification platforms. Moreover, the attitude changes of UAV is analyzed by the simulation and flight test, respectively. In addition, an adaptive factor is used to adjust the measurement noise covariance in order to eliminate the harmful effects of linear acceleration in the accelerometer, which is solved the roll and ptich angle. The experimental comparison with the Complementary Filter shows that the proposed algorithm can provide accurate attitude information when UAV is flying, which improves the accuracy and reliability of attitude solution, and removes the influence the gyro bias for the attitude estimation.