Abstract
The instantaneous wind field and air data, including true airspeed, angle of attack, angle of sideslip, cannot be measured and recorded accurately in wind disturbance. A new air data and wind field estimation method is proposed based on flight data in this study. Since the wind field is the horizontal prevailing wind added by turbulence, the slowly time-varying prevailing wind and small-scale turbulence are described by the exponentially correlated stochastic wind model and von Karman turbulence model, respectively. The system update equation of air data is built based on inertial measurements instead of the complex aerodynamic and aero-engine model of aircraft. Benefitted by the post-analysis characteristics of flight data, a forward–backward filtering algorithm was designed to improve the estimation accuracy. Simulation results indicate that the forward–backward filter integrated with the von Karman turbulence model can reduce the estimation error and ensure filtering stability. A further test with actual flight data shows that the forward–backward filter is not only able to track the wide-range change in prevailing wind but also reduce the adverse effects of uncertain disturbance on estimation accuracy.
Highlights
Flight data, recorded by the airborne flight data acquisition system, provide a fundamental way to analyze flight quality and accident [1]
The Air Data System (ADS) is unable to respond in time and compute accurately due to the rapid change in wind disturbance, which brings about measuring error of air data
This paper deals with the estimation of the wind field and three air data, including true airspeed, angle of attack, angle of sideslip, in wind disturbance based on the flight data of civil aviation aircraft
Summary
Flight data, recorded by the airborne flight data acquisition system, provide a fundamental way to analyze flight quality and accident [1]. This paper deals with the estimation of the wind field and three air data, including true airspeed, angle of attack, angle of sideslip, in wind disturbance based on the flight data of civil aviation aircraft. These air data are important to compute accurate aerodynamic performance and beneficial for flight quality and accident analysis. Given that the flight data is recorded in real-time but decoded and analyzed afterward, a forward–backward filtering algorithm is designed, in which the backward filtering is used to improve the accuracy further. The test with real flight data is further analyzed to verify the effectiveness of the method
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