UAV Flight Test Evaluation of Fusion Algorithms for Estimation of Angle of Attack and Sideslip Angle

Moving Horizon Estimation of Air Data Parameters for UAVs

“…One popular methods is the Extended Kalman Filter (EKF), which has been used in [11] , [21], [24], and the Unscented Kalman Filter (UKF) which has been applied to the problem in [33], [12]. Tian et.al [40] compare the use of an EKF, Output Error Minimization and a Complimentary Filter to improve measurements of the air data parameters obtained from a multi-hole probe. [8] uses a detailed aircraft model and a nonlinear observer for wind estimation.…”

Real-Time Fault-Tolerant Moving Horizon Air Data Estimation for the RECONFIGURE Benchmark

“…Moreover, the aerodynamic coefficients highly depend on the specific aircraft structure and flight envelope, thus the corresponding FDI and estimation methods might fail in any unexpected condition [16]. In contrast, the second category adopts an aerodynamicsindependent model, e.g., the wind velocity triangle [17]- [19], the aircraft dynamic model with three-axis load factors as inputs [20]- [22], or a combination of the above two models [16], [23]. Such aerodynamics-independent models simplify the design of FDI and estimation algorithms by avoiding the use of uncertain aerodynamics, hence the corresponding algorithms can be easily configured for different aircraft without adapting to the changing aerodynamics [16], [20].…”

Combining model-free and model-based angle of attack estimation for small fixed-wing UAVs using a standard sensor suite

“…One popular methods is the EKF, which has been used in [4] , [10], [12], and the Unscented Kalman Filter which has been applied to the problem in [13], [5]. Tian et.al [16] compare the use of an EKF, Output Error Minimization and a Complimentary Filter to improve measurements of the airflow variables obtained from a multi-hole probe. [3] uses a detailed aircraft model and a nonlinear observer for wind estimation.…”