Abstract
An improved Sage-Husa adaptive extended Kalman filter algorithm is proposed to ensure the precision and stability of calculating attitude angles of a multi-rotor Unmanned Aerial Vehicle (UAV) under the actual flight conditions, such as unknown and time-varied noise statistical properties, main disturbance source in vibration and attitude angles high dynamically changed. The algorithm uses attitude angle variance estimated by a gyroscope in real time to estimate system noise variance and only adopts an adaptive filter algorithm to estimate measurement noise variance on-line to ensure the precision and stability of filtering. Meanwhile, it introduces the criterion of filter convergence to restrain the divergence of Kalman filter through combining with a strong tracking Kalman filter algorithm. A flight experiment and corresponding analysis show that the root-mean-square errors of the pinch and roll angles estimated by the improved algorithm are 1.722 and 1.182, obviously better than that of the conventional Sage-Husa adaptive Kalman filter algorithm. It concludes that the improved algorithm has strong adaptive ability, good real-time performance, high precision and reliable operation. It meets the need of multi-rotor UAV autonomous flight and can be applied to other navigation information measuring systems with high dynamic performance requirements if the parameters are modified appropriately.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: 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.