Airborne Machine Learning Estimates for Local Winds and Kinematics

Future Unmanned Aerial Systems (UAS) and air taxis will require advanced onboard autonomy to operate safely within complex and dynamic urban environments. Urban landscapes are dynamic and constantly evolving. In addition to multi-directional, intense, and seemingly unpredictable winds often created in urban canyons, an exact knowledge of current building sizes, shapes, and positions is also often unavailable for real-time navigation.