Solar UAV endurance boost with UKF-based thermal updraft tracking

This paper presents a novel pitching moment model designed for solar-powered Unmanned Aerial Vehicles (UAVs) operating within thermal updraft environments, drawing upon the conceptual framework of thermal updraft modeling. The model demonstrates its versatility by finding application in tasks related to environmental thermal updraft positioning and tracking, all facilitated through the use of a state Unscented Kalman Filter (UKF) estimation algorithm. The simulation outcomes convincingly affirm the substantial advantages brought about by the introduction of the solarpowered UAV pitching moment model within the framework of thermal updraft state estimation. When pitted against the use of a standalone thermal updraft model or a solar-powered UAV thermal updraft field rolling moment model, the algorithm exhibits remarkable enhancements in both convergence speed and accuracy when it comes to locating the thermal center. These improvements signify the pertinence and effectiveness of the proposed model, establishing it as a valuable addition to the arsenal of tools available for optimizing solar-powered UAV operations within thermal updraft fields. The novel model's contributions have the potential to significantly advance the capabilities of solar-powered UAVs, enabling them to more efficiently harness the benefits of thermal updrafts in various environmental monitoring and surveillance applications.