A main problem in autonomous vehicles in general, and in 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 AHRS based on the UKF using the TRIAD algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the EKF . The paper presents field experiment results using a real fixed-wing UAV . The results show good real-time performance with low computational cost in a microcontroller.
A new adaptive Unscented Kalman Filter (UKF) algorithm for actuator failure estimation is proposed. The novel filter method with adaptability to statistical characteristic of noise is presented to improve the estimation accuracy of traditional UKF. The algorithm with the adaptability to statistical characteristic of noise, named Kalman Filter (KF) -based adaptive UKF, is proposed to improve the UKF performance. Such an adaptive mechanism is intended to compensate the lack of a prior knowledge. The asymptotic property of the adaptive UKF is discussed. The Actuator Healthy Coefficients (AHCs) is introduced to denote the actuator failure model while the adaptive UKF is employed for on-line estimation of both the flight states and the AHCs parameters of rotorcraft UAV (RUAV). Simulations are conducted using the model of SIA- Heli-90 RUAV of Shenyang Institute of Automation, CAS. The results are compared with those obtained by normal UKF to demonstrate the effectiveness and improvements of the adaptive UKF algorithm. Besides, we also compare this algorithm with the MIT-based one which we propose in previous research.