Abstract
This letter develops a framework for online state, parameter, and wind estimation for a UAV equipped with an Inertial Measurement Unit (IMU) and a ground-velocity sensor, such as visual- or lidar-based odometry. Thrust and moment control inputs are used to steer the process model and the ground-velocity and IMU measurements are assimilated by a square-root unscented Kalman filter containing 23 states, 12 of which are constant parameters. Additionally, we characterize the system's observability and design optimal calibration trajectories to maximize observability of the model parameters. Simulations show the improvements obtained in tracking performance by coupling the estimation framework with a standard model-based controller. By estimating unsteady winds, the onboard controller's gust rejection improves.
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.
