An attitude estimate method for fixed-wing UAV s using MEMS/GPS data fusion

Unmanned Aerial Vehicles(UAVs) require high performance Attitude and Heading Reference System(AHRS) in automatic flight. This paper presents an attitude estimate method using Micro Electro Mechanical Systems(MEMS) and GPS data fusions. An Extended Kalman Filter(EKF) is developed in the algorithm. The measurements of MEMS Inertial Measuring Units(IMUs) and GPS receiver are modeled. The observation equations of EKF are simplified by the augmentation of GPS-derived accelerations and accelerometer measurements, which can reduce computational overhead. Simulation tests show the effectiveness of the proposed attitude estimate method.