Method of Estimating Angle-of-Attack and Sideslip Angel Based on Data Fusion

Lian, Hao; Song, Shujie

doi:10.1109/icicta.2009.160

Cited by 19 publications

(9 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The estimator proposed in [8] does not need the aerodynamics of the aircraft neither other aircraft parameters; but the method works properly only if the aircraft dynamics are excited by continuously changing pitch and yaw angles. Model-based estimation methods that do not use the Kalman Filter are proposed in [10] and [11]; both these approaches require as input a detailed model of the aircraft aerodynamics. In [10] the data fusion of GPS (Global Positioning System) and IMU (Inertial Measurements Unit) sensors together with the aerodynamic information allow computing the angle of attack, whereas in [11] a Bayesian estimator provides it.…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

Vitale¹,

Corraro²,

Genito³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

The angle of attack is a critical flight parameter for commercial aviation aircraft, because automatic envelope protection systems rely on it to keep the aircraft within its safe flight envelope. Faulty measurements of the angle of attack could have catastrophic effects, leading to aircraft loss of control in flight and fatalities, as demonstrated by the recent accidents involving the Boeing 737-MAX. This paper presents a novel approach to the measurement of the angle of attack, which uses one virtual sensor and two physical sensors to implement a physical-analytical redundant system that is robust to a single fault of the physical sensors. The virtual sensor is based on an innovative and reliable estimator of the angle of attack. It was originally developed to provide General Aviation pilot with an accurate indication of trend toward stall, and has been suitably customized to fit its application to commercial aviation. One of the peculiarities of the redundant measurement system is that its implementation on-board several existing commercial aviation aircraft only needs the integration of a software code and does not require any installation of additional physical sensors. The proposed approach demonstrated very interesting performance, assessed in simulation through several Monte Carlo analyses. Its exploitation could contribute to reduce the angle of attack related accidents, improving the safety of the air transport system.

show abstract

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

Vitale¹,

Corraro²,

Genito³

et al. 2021

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…In the second flight a race track pattern is flown at a higher altitude of 90m above ground. During this flight the airspeed was varied between 14m/s and 26m/s to vary the angle of by first calculating the relative airspeed vector using (24) and afterwards the wind velocity vector with the wind triangle (1).…”

Section: A X8 Flights 1) Flight Pathmentioning

confidence: 99%

“…Recently several methods have been proposed to estimate the air data parameters and aerodynamic coefficients. 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.…”

Section: Introductionmentioning

confidence: 99%

Moving Horizon Estimation of Air Data Parameters for UAVs

Wenz

Johansen

2020

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

Local wind velocities, angle of attack and lift coefficients of a fixed-wing unmanned aerial vehicle (UAV) are estimated by fusing kinematic, aerodynamic and stochastic wind models with data from an inertial measurement unit, a global navigation satellite system receiver and a pitot-static tube in a Moving Horizon Estimator. Experimental validation with two different UAVs and two sensor sets of different quality, show promising results for both wind velocity and angle of attack estimation.

show abstract

“…Four different wind velocity estimation methods, using kinematic models combined with different sensor sets in a UKF are studied in [11]. Popular estimation methods to combine kinematic models with sensor data are the Extended Kalman Filter (EKF), which has been used in [12]- [14], and the Unscented Kalman Filter (UKF) which has been applied to the problem in [15], [16]. Wind velocity estimation methods based on kinematic models are easier to apply to the scenario of small UAVs.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Wenz

Johansen

2020

2020 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

We present a real-time implementation of an estimation algorithm for angle of attack, airspeed and wind velocities estimation on a single board computer. The estimator uses only sensor data from a standard fixed-wing UAV autopilot, which consists of a Global Navigation Satellite System receiver, an inertial measurement unit and a pitot-static tube. This sensor data is fused with a combination of kinematic, aerodynamic and stochstic wind models in a nonlinear moving horizon estimator using numerical optimization. An algorithmic differentiation toolbox and automatic code generation is used to create a realtime capable estimator which is able to run within a UAV on an on-board computer. Hardware in the Loop simulation results show that the latency of the estimator is significantly below the expected wind gust period and gives low root-mean-square estimation errors for angle of attack (0.29 •) , airspeed (0.21m/s) and wind velocities (0.44m/s).

show abstract

Method of Estimating Angle-of-Attack and Sideslip Angel Based on Data Fusion

Cited by 19 publications

References 4 publications

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

An Innovative Angle of Attack Virtual Sensor for Physical-Analytical Redundant Measurement System Applicable to Commercial Aircraft

Moving Horizon Estimation of Air Data Parameters for UAVs

Real-Time Moving Horizon Estimation of Air Data Parameters and Wind Velocities for fixed-wing UAVs

Contact Info

Product

Resources

About