Performance Analysis of the Thermal Automatic Tracking Method Based on the Model of the UAV Dynamic Model in a Thermal and Cubature Kalman Filter

Abstract

Fixed-wing, solar-powered unmanned aerial vehicles (SUAVs) can use thermals to expand the duration of flight. Nevertheless, due to the demand for calculating the thermal state parameters of the SUAV during flight, the existing methods still have some shortcomings in their practical applications, such as an inaccurate location estimation of the thermal and an insufficient seeking efficiency. In this paper, by integrating the Gaussian distribution model of thermal updraft of the pitching and roll moment of SUAVs, it is demonstrated that the approach introduced is superior to the traditional methods, disregarding the pitching moment. The simulation indicated that the accuracy and convergence speed of the thermal state estimation, performed while employing the cubature Kalman filter (CKF), are significantly improved after the SUAVs pitching moment is considered. The proposed method improves the automaticity and intelligence of SUAVs autonomous thermal search and enhances the cognitive and decision-making capabilities of SUAVs.