  Due to the state trajectory-independent features of invariant Kalman
filtering (InEKF), it has attracted widespread attention in the research
community for its significantly improved state estimation accuracy and
convergence under disturbance. In this paper, we formulate the full-source 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 multi-rate onboard
sensor network on UAVs to achieve 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 an Long Short Term Memory (LSTM) deep 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 dependency on GNSS or complicated aerodynamic model
parameters. Further, we validate the algorithm's robust advantages under GNSS
denied, where flight data shows that the maximum positioning error stays within
30 meters over a 130-second denial period. To the best of our knowledge, this
study is the first to apply ES-RIEKF to full-source navigation applications for
fixed-wing UAVs, aiming to provide engineering references for designers. Our
implementations using MATLAB/Simulink will open source.
