A ‘No-Flow-Sensor’ Wind Estimation Algorithm for Unmanned Aerial Systems

Abstract

A ‘no-flow-sensor’ wind estimation algorithm for Unmanned Aerial Systems (UAS) is presented. It is based on ground speed and flight path azimuth information from the autopilot's GPS system. The algorithm has been tested with the help of the simulation option in the Paparazzi autopilot software using artificial wind profiles. The retrieval accuracy of the predefined profiles by the wind algorithm and its sensitivity to vertical aircraft velocity, diameter of the helical flight pattern and different data sampling methods have been investigated. The algorithm with a correspondingly optimized set of parameters is then applied to various scientific flight missions under real wind conditions performed by the UAS SUMO (Small Unmanned Meteorological Observer). The SUMO wind profiles are compared to measurements of conventional atmospheric profiling systems as radiosondes and piloted balloons. In general, the presented ‘no-flow-sensor’ wind estimation method performs well in most atmospheric situations and is now operationally used in the post-processing routine for wind profile determination from SUMO measurements.