An adaptive attitude algorithm based on a current statistical model for maneuvering acceleration

A current statistical model for maneuvering acceleration using an adaptive extended Kal-man filter (CS-MAEKF) algorithm is proposed to solve problems existing in conventional extended Kalman filters such as large estimation error and divergent tendencies in the presence of continuous maneuvering acceleration. A membership function is introduced in this algorithm to adaptively modify the upper and lower limits of loitering vehicles' maneuvering acceleration and for real-time adjustment of maneuvering acceleration variance. This allows the algorithm to have superior static and dynamic performance for loitering vehicles undergoing different maneuvers. Digital sim-ulations and dynamic flight testing show that the yaw angle accuracy of the algorithm is 30%better than conventional algorithms, and pitch and roll angle calculation precision is improved by 60%. The mean square deviation of heading and attitude angle error during dynamic flight is less than 3.05°. Experimental results show that CS-MAEKF meets the application requirements of miniature loitering vehicles.