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.

翻译：由于不变卡尔曼滤波（InEKF）的状态轨迹独立性特征，其在扰动下显著提升状态估计精度与收敛性的优势引起了研究界的广泛关注。本文在基于李群的误差状态右不变扩展卡尔曼滤波（ES-RIEKF）框架下，构建了固定翼无人机全源数据融合导航问题。我们融合无人机多速率机载传感器网络的测量值，实现了位姿、气流角和风速的实时估计。文中给出了详细推导，并通过无人机真实飞行数据证明，该算法相较误差状态扩展卡尔曼滤波（ES-EKF）和非线性互补滤波器（NCF）等传统方法在收敛性和精度上均有提升。此外，我们提出了一种仅依赖地面可测参数的半气动模型融合框架，设计并训练了长短期记忆（LSTM）深度学习网络，通过易于获取的机载数据（如舵面偏转角）实现对无人机迎角和侧滑角的无漂移预测，从而显著降低了对全球卫星导航系统（GNSS）或复杂气动模型参数的依赖。进一步，我们在GNSS拒止环境下验证了该算法的鲁棒性优势——飞行数据显示，在130秒的拒止时段内，最大定位误差保持在30米以内。据我们所知，本研究首次将ES-RIEKF应用于固定翼无人机全源导航场景，旨在为设计人员提供工程参考。我们的MATLAB/Simulink实现将开源。