用于无人机全状态估计的半气动模型辅助不变卡尔曼滤波
Semi-Aerodynamic Model Aided Invariant Kalman Filtering for UAV Full-State Estimation
AI总结：
本研究首次将ES-RIEKF应用于固定翼无人机全源导航，结合半气动模型与LSTM网络实现无漂移气流角预测，提升了GNSS拒止下的状态估计精度与鲁棒性。
摘要：
不变卡尔曼滤波（InEKF）因具备与状态轨迹无关的特性，在扰动下的状态估计精度和收敛性显著提升，受到研究界广泛关注。本文在李群上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。我们融合无人机上多速率机载传感器网络的测量值，实现对位姿、气流角和风速的实时估计。文中给出了详细推导，并利用无人机真实飞行数据，验证了该算法相比误差状态扩展卡尔曼滤波（ES-EKF）、非线性互补滤波器（NCF）等成熟方法，在收敛性和精度上的提升。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。设计并训练了一个长短期记忆（LSTM）深度网络，利用操纵面偏转角等易获取的机载数据，实现无人机迎角（AOA）和侧滑角（SA）的无漂移预测，从而显著降低对GNSS或复杂气动模型参数的依赖。进一步地，我们验证了算法在GNSS拒止环境下的鲁棒优势，飞行数据显示，在130秒的拒止周期内，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。我们基于MATLAB/Simulink的实现代码将开源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
