  Aimed at solving the problem of Attitude and Heading Reference System(AHRS)
in the complex and dynamic conditions for small-UAV, An intelligent Singular
Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable
Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity
of quaternion AHRS model and non-positive definite of the state covariance
matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to
better obtain the filter accuracy and reliability. Additionally, there are the
different changes of the values in the accelerometer measurement resulting from
the complex flying conditions. Thus, the VAM is designed to deal with
three-axis values of the acceleration and tune intelligently the measurement
noise matrix Ra. Moreover, the heading measurement from the three-axis values
of the magnetometer is calculated according to the whether to use the
three-axis values of the acceleration in the special situations. The simulation
and experiment results demonstrate that the proposed filter algorithm has the
more excellent attitude solution accuracy and robustness than both the
Complementary Filter(CF) and the Error State Kalman Filter(ESKF).
