Small UAS-based wind feature identification system. Part 1: Integration and validation

Abstract

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 UnmannedAerialSystems(UAS).Theproposedsystemgeneratesreal-timewindvectorestimatesand a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelfnavigationsystemandairspeedreadingsinaso-calleddirectapproach. Windpredictions 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 aWeibullprobabilitydensityfunction. AGeneticAlgorithm(GA)isutilizedtodeterminetheshaping and scale parameters of the distribution, which are employed to determine the most probable wind speedatacertainposition. Thesystemusesthisinformationtocharacterizeawindshearoradiscrete 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 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 1Hz and a wind map can be updated at 0.4Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30s.