UAV attitude estimation using Unscented Kalman Filter and TRIAD
This addresses the need for accurate and efficient attitude estimation in autonomous UAVs, representing an incremental improvement over existing methods like EKF-based systems.
The paper tackled the problem of estimating attitude angles for UAVs by proposing a novel method using an Unscented Kalman Filter with TRIAD as the observation model, achieving good real-time performance with low computational cost in a microcontroller.
A main problem in autonomous vehicles in general, and in \acp{UAV} in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an \ac{AHRS} based on the \ac{UKF} using the \ac{TRIAD} algorithm as the observation model. The performance of the method is assessed through simulations and compared to an \ac{AHRS} based on the \ac{EKF}. The paper presents field experiment results using a real fixed-wing \ac{UAV}. The results show good real-time performance with low computational cost in a microcontroller.