A Sigma-Point Kalman Filter for Remote Sensing of Updrafts in Autonomous Soaring

Abstract

Autonomous soaring is a promising approach to augment the endurance of small UAVs. Most of the existing work on this field relies on accelerometers and/or GPS receivers to sense thermals in the proximity of the vehicle. However, thermal updrafts are often visually indicated by cumulus clouds that are well char- acterized by their sharp baselines. This paper focuses on a cloud mapping algorithm which estimates the 3D position of cumulus clouds. Using the meteorological fact of a uniform cloud base altitude a state-constrained sigma-point Kalman filter (SC- SPKF) is developed.A method of using the resulting cloud map and its uncertainty in the path planning task to realize a soaring flight to a given wayoint is presented as a perspective of this work. 1