Naive Kalman filtering for estimation of spatial object orientation

In the paper an efficient and accurate method for estimating object orientation in three-dimensional (3D) space is proposed. Classical approaches based on Kalman filtering requires mathematical formulation of plant model, which in most cases is based on the nonlinear equations of rotational kinematics of rigid bodies. It follows that linearization operations are necessary. This approach is correct but in many cases leads to difficulties in computations and implementations. To simplify this problem, using the assumption of Bayesian classification systems, in the paper the angular velocity vector is treated as three separate events. Therefore, tree independent Kalman filters are used to estimate Euler angles for each Roll-Pitch-Yaw coordinate system. This new approach is called Naive Kalman Filter. Data fusion for real IMU sensor which integrates data from triaxial gyroscope, accelerometer and magnetometer is presented in order to illustrate accuracy and computational efficiency of proposed filter.