Wind-field reconstruction from flight data using an unbiased minimum-variance unscented filter

Although guidance of all aircraft is affected by wind disturbances, micro-unmanned aerial vehicles are especially susceptible. To estimate unknown wind disturbance, we consider two illustrative scenarios for planar flight. In the first scenario, we assume that measurements of the heading angle are available, while, in the second scenario, we assume that measurements of the heading angle are not available. Since the disturbance estimation problem is non-linear, we develop an extension of the unscented Kalman filter that provides an estimate of the unknown wind disturbance. Furthermore, we show through simulations that, when the heading angle is not measured, a kinematic ambiguity is introduced. However, when the initial heading angle is known and the subsequent heading angle is not measured, this kinematic ambiguity is resolved and accurate estimates of the wind velocity are obtained.