Semi-Aerodynamic Model-Aided Invariant Kalman Filtering for UAV Full-State Estimation

Due to the state trajectory-independent features of invariant extended Kalman filtering, it has attracted widespread attention in the research community for its significantly improved state estimation accuracy and convergence under disturbance. In this article, we formulate the full-state data fusion navigation problem for fixed-wing unmanned aerial vehicle (UAV) within a framework based on error-state right-invariant extended Kalman filtering (ES-RIEKF) on Lie groups. We merge measurements from a multirate onboard sensor network on UAVs to achieve a real-time estimation of pose, air flow angles, and wind speed. Detailed derivations are provided, and the algorithm’s convergence and accuracy improvements over established methods like error-state EKF (ES-EKF) and nonlinear complementary filter (NCF) are demonstrated using real-flight data from UAVs. Additionally, we introduce a semi-aerodynamic model fusion framework that relies solely on ground-measurable parameters. We design and train a long short-term memory (LSTM) network to achieve drift-free prediction of the UAV’s angle of attack (AOA) and side-slip angle (SA) using easily obtainable onboard data like control surface deflections, thereby significantly reducing dependence on global navigation satellite systems (GNSS) or complicated aerodynamic model parameters. Further, we validate the algorithm’s robust advantages under GNSS denied, where flight data show that the maximum positioning error stays within 30 m over a 130-s denial period. To the best of authors’ knowledge, this study is the first to apply ES-RIEKF to full-state navigation applications for fixed-wing UAVs, aiming to provide engineering references for designers. Our implementations using MATLAB/Simulink will be open source.