Wind field estimation and identification having shear wind and discrete gusts features with a small UAS

This paper presents a new method for estimation and identification of shear wind and discrete gusts of a previously unknown wind field by using an Unmanned Aircraft System (UAS). Wind estimation and identification is key in energy-efficient trajectory planning and dynamic soaring applications. The research proposes an approach for mapping a complete wind field from the collected data. Therefore, the generated map also describes areas where UAS has not passed through. The proposed method consists of the next steps: 1) the wind vector is estimated in each UAS position; 2) wind data are fitted into a Weibull probability density function and meeting the Prandtl's power law relationship; 3) scale factor of the Weibull distribution and the power law coefficient are computed; 4) wind feature detection such as shear layer and gusts is performed from the relation wind magnitude vs. altitude obtained; and finally 5), data could be extrapolated to generate the complete wind field. Novel aspects and advantages include the optimization of the scale factor from the estimated wind data by using a genetic algorithm, the identification of wind features separately, and the possibility to apply the method online. Real data of flight have been used to validate the method and many simulations and studies have been performed to test and analyze the proposed method in different scenarios.