基于Kalman滤波的高空长航时无人机近似风场预测研究

Research on Approximate Wind Field Prediction of HALE UAV Based on Kalman Filter

针对高空长航时无人机在飞行过程中对风场敏感但动态快速预测风况存在困难的问题,设计了一种基于Kalman滤波的高空长航时无人机近似风场预测系统。该系统采用机上常用的多余度传感器获取绝对速度地速、牵连速度真空速,然后通过速度矢量三角形获取相对速度风速。通过北-东-地惯性坐标系和机体坐标系下的矩阵转换,引入Kalman滤波算法对所得数据进行融合处理,可实时获得近似风场的风速和风向。采用某低动态高空长航时无人机开展了中高空飞行试验来对该风场预测系统进行测试,有效动态捕捉到了无人机飞行过程中的风场变化信息,并以此在地面站上在线调整优化了无人机飞行航迹,提升了飞行安全性,对应急情况可提供强有力支撑。该风场预测系统简单、有效,实用性强,具有工程指导意义。

Aiming at the problem that the HALE(High Altitude Long Endurance) UAV(Unmanned Aerial Vehicle) is sensitive to the wind field during flight but has difficulty in predicting wind conditions dynamically, a Kalman filter-based approximate wind field prediction system is proposed and designed. The system adopts the redundant sensors commonly used on the aircraft to obtain the absolute speed which represents ground speed, the entrainment speed which represents true airspeed, and then obtains the relative speed which represents wind speed through the wind triangle relationship. Furthermore, through the transformation matrix between the North-East-Earth inertial coordinate system and the body coordinate system, the Kalman filter algorithm is introduced to fuse the data and obtain the wind speed as well as wind direction in real time approximately. Moreover, a HALE UAV with low velocity was used to conduct a midhigh altitude flight test in order to verify the designed wind field prediction system. The results show that the designed wind field prediction system has captured the wind field variation information during the flight effectively, and this guides the operator around ground control station to adjust and optimize the flight path of the UAV On-line, which improves safety flight test and provides strong support for emergency situations. The wind field prediction system designed is simple, effective and practical, which makes it available in engineering applications.