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 'noflow-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.

Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR)—The Hailuoto 2017 Campaign

“…The meteorological sensors for T and RH are placed a fair distance from the battery and motor on top of the wings to assure good ventilation during flight. In addition to the directly-measured meteorological parameters, like temperature, relative humidity, and pressure, the horizontal wind speed and direction can be estimated by applying the "no-flow-sensor" wind estimation algorithm described in reference [68]. The Multi-purpose Airborne Sensor Carrier (MASC-2) is an electrically-powered, single engine, pusher aircraft of 3.5 m wing span and a total weight of 6 kg, including a scientific payload of 1.0 kg [85].…”

“…In addition to the directly measured parameters, like T, RH, and p, obtained by the SUMO, the horizontal wind speed (U) and direction (Dir) were estimated by applying the "no-flow-sensor" wind estimation algorithm described by [68]. All SUMO data were interpolated to a common frequency of 4 Hz in order to provide a consistent data set.…”

Tightly Coupled Navigation and Wind Estimation for Mini UAVs

“…RIt may be useful to utilize fleets of UAVs to map the wind, e.g., for meteorological purposes, gas mapping, [6][7][8][9] with several advantages over manned flight as low cost or simplicity of deployment. However, such mapping procedures usually assume the local wind estimation, i.e., the real-time estimation at the current location of the UAV, as an output of a yet established black-box, whereas it strongly a↵ects the mapping quality.…”

“…13 Van den Kroonenberg et al 14 assume the horizontal wind speed as constant and no vertical wind during one flight loop and proposes then an experimental estimator through Kalman filtering. Similar estimators with no "flow sensor" have been tested, 8,9 but an accurate 3D wind estimation need additional sensors, 15 e.g., Pitot tube, pressure strip or vanes. 7 With an added Pitot tube, Cho et al 16 design a relatively simple nonlinear Kalman filter for horizontal wind estimation.…”

“…Finally, validating such wind estimators in experimental flight is challenging due to the di culty in accurately measuring the wind. One can yet use ground weather station data, radiosondes and piloted balloons, or SOnic Detection and Ranging, 8,[14][15][16][17] albeit only an approximation to the local wind at the aircraft.…”