The proposed AUKF tunes its measurement covariance to yield optimal performance. Design/methodology/approach – A recursive least-square algorithm with exponential age weighting in time is utilized for estimation of the unknown inputs. Purpose – This paper aims to develop an adaptive unscented Kalman filter (AUKF) formulation for orientation estimation of aircraft and UAV utilizing low-cost attitude and heading reference systems (AHRS). The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The algorithm outlined in this work produces a state estimate for the height and attitude of a rotorcraft with any number of rotors that are fixed pitch. Furthermore, we evaluated the implementation on the PX4FMU micro-controller of the pixhawk project. We tested the algorithm using real sensor data in a specifically developed Matlab framework, where direct and indirect variations of an EKF, as well as a non-linear Unscented Kalman Filter (UKF), are compared. We have developed an Extended Kalman Filter (EKF) that uses rigid body dynamics for the prediction, while transforming the information of the sensor signals to information about the states for the correction. Therefore, we introduce a state estimation algorithm for a quadrotor using an inertial measurement unit (IMU), a barometer and a sonar sensor. The goal of this work was to enable the UAV to hover and land safely without relying on optical sensors. Especially for stabilizing the attitude of an unmanned aerial vehicle (UAV), a high degree of precision and accuracy is required. For most autonomous robots it is crucial to know where the robot is located and where it is heading to, so it can plan its next steps.
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