Estimation of Attitude Using Robust Adaptive Kalman Filter

This paper proposes a novel covariance-scaling based robust adaptive Kalman filter (RAKF) algorithm for attitude (i.e., roll and pitch) estimation using an inertial measurement unit (IMU) composed of accelerometer and gyroscope triads. KF based and complementary filtering (CF) based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is difficult and time-consuming task. The proposed algorithm provides an adaptive way for tuning process and it can accurately estimate the attitude in two axes. The proposed methodology is tested and compared with other existing filtering methodologies in the literature under different dynamical conditions and using real-world experimental dataset in order to validate its effectiveness.