Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
