This paper presents a system for identification of wind features, such as gusts and wind shear.These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS).The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions.
Estimations are based on the integration of an off-the-shelf Breast Shields navigation system and airspeed readings in a so-called direct approach.Wind predictions use atmospheric models to characterize the wind field with different statistical analyses.During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations.
Wind estimates are classified and fitted into a Weibull probability density function.A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position.The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts.
The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload.Therefore, the system provides a solution that does not require any additional sensors.The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability Poster and maintainability.
Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing.Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing.Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.
4 Hz.Predictions show a convergence time with a 95% confidence interval of approximately 30 s.