Integrated Attitude Estimate Algorithm for INS/Magnetometer System Based on Data Fusion of Dual MIMU Inertial Array

In this paper, a MIMU/magnetometer attitude estimate algorithm based on the data fusion of an inertial measurement array is proposed for the micro unmanned aerial vehicle. A parallel and co-directional measurement array is constructed by using two MIMU units, and a Kalman filter (KF) is presented for fusing multiple gyro signals from MIMU array to achieve an optimal estimation of the tri-axial input rate, and then the estimated rate signals from fused MIMU are employed to resolve the aircraft’s attitude quaternion. Furthermore, the quaternion error and drift noise of inertial sensors are chosen to construct a KF state vector, and an integrated KF is established to obtain the optimal attitude quaternion, specially a modified Gauss-Newton iterative approach is utilized to get the matched quaternion making use of gravitational and geomagnetic field information to construct the KF measurements. The simulations and experiments demonstrated that the estimated error of attitude quaternion using fusion of MIMU array is less than that by a single MIMU. The estimated errors for quaternion components of q₀ and q₃ are about 9.4% of the errors caused by the original signals of single MIMU, and the estimated errors for component q₁ and q₂ are about 15.3% and 17.3%, respectively. The results show that the proposed KF algorithm can effectively decrease the drift noise of MIMU and improve the attitude estimation accuracy.