Weight Self-Adjustment Adams Implicit Filtering Algorithm for Attitude Estimation Applied to Underwater Gliders

High-accuracy attitude estimation plays an important role in gliding with long endurance for an underwater glider. Because microelectromechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption, they are used as main sensors to determine navigation information. However, in the complicated and harsh underwater environment, the performances of MEMS sensors degrade and errors will become larger. Moreover, acceleration or deceleration while gliders going up and down, sudden vibration of gliders due to inevitable disturbances will bring larger errors for sensors. So it is difficult to acquire the high accuracy attitude calculated by inertial measurement unit. In order to solve the above problem, first, a novel weight self-adjustment extended Kalman filtering method, which can adjust the weight autonomously through estimating adaptively measurement noise, is proposed to perform the optimal error estimation. Moreover, a fusion method that integrates the Adams implicit formula with the weight self-adjustment filtering method is proposed to achieve the more improvement in attitude estimation accuracy. The performance of this proposed algorithm is evaluated by the theoretical proofs and simulations. Subsequently, it is tested by the ship experiments and the lake trials. The results show that this proposed algorithm has a better performance in terms of attitude estimation accuracy than extended Kalman filtering (EKF)-only and self-adjustment EKF in this paper. Meanwhile, this algorithm has good robustness for attitude calculation even though pitch angle changes large.