Flight Data-Based Wind Disturbance and Air Data Estimation

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.

Refined Aircraft Positioning Based on Stochastic Hybrid Estimation with Adaptive Square-Root Unscented Particle Filtering

“…Under normal circumstances, the system update and measurement update are carried out simultaneously. When there are no measurements, only the system update is executed by unequal interval filtering [30].…”