  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.
