A Low-Cost System for Measuring Horizontal Winds From Single-Engine Aircraft (original) (raw)
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Small Unmanned Aircraft Systems (SUAS) are an emerging technology that is suitable for multitude of applications requiring small sensor payloads including airborne wind measurement. This work discusses the considerations and limitations of different SUAS configurations, in particular Multi-rotor UAS (MUAS), and their capabilities when operating within the Atmospheric Boundary Layer (ABL). Several methods for measuring fluctuating flows from SUAS are discussed, and preliminary results from a quadrotor-mounted Multi-Hole Pressure Probe (MHPP) "flying anemometer" platform are presented. Flights at a range of altitudes demonstrated that in-situ measurements of both mean wind velocity and turbulence intensity from hovering airborne platform are feasible. This indicates that MUAS can be used as flexible wind sensing platforms with good high spatial resolution. Suggestions for how future SUAS technological and operation developments may further improve wind engineering applications are also discussed.
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Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using well aspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.
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Recently, there has been an increase in use of Unmanned Aerial Systems (UASs) as platforms for conducting fundamental and applied research in the lower atmosphere due to their relatively low cost and ability to collect samples with high spatial and temporal resolution. Concurrent with this development comes the need for accurate instrumentation and measurement methods suitable for small meteorological UASs. Moreover, the instrumentation to be integrated into such platforms must be small and lightweight. Whereas thermodynamic variables can be easily measured using wellaspirated sensors onboard, it is much more challenging to accurately measure the wind with a UAS. Several algorithms have been developed that incorporate GPS observations as a means of estimating the horizontal wind vector, with each algorithm exhibiting its own particular strengths and weaknesses. In the present study, the performance of three such GPS-based wind-retrieval algorithms has been investigated and compared with wind estimates from rawinsonde and sodar observations. Each of the algorithms considered agreed well with the wind measurements from sounding and sodar data. Through the integration of UAS-retrieved profiles of thermodynamic and kinematic parameters, one can investigate the static and dynamic stability of the atmosphere and relate them to the state of the boundary layer across a variety of times and locations, which might be difficult to access using conventional instrumentation.
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A new method for estimating the wind field velocity and acceleration is proposed, which properly relates the kinematics of the aircraft using the aircraft frame, wind frame, and Earth-fixed frame. The proposed technique was compared to an existing direct method for computing the wind field velocity. Experimental Unmanned Aerial Vehicle (UAV) flight data was used to validate the proposed approach. The experimental results demonstrated effective estimation of the attitude angles, and provided a smoothed estimate of the airspeed, angle of attack, and sideslip angle. The wind estimation results were validated with respect to measurements provided by a local weather station. It was shown that this new method is capable of providing a much more reasonable estimate of the local wind field than the existing direct method.
2015 International Conference on Unmanned Aircraft Systems (ICUAS), 2015
It is proposed to estimate wind velocity, Angle-Of-Attack (AOA) and Sideslip Angle (SSA) of a fixed-wing Unmanned Aerial Vehicle (UAV) using only kinematic relationships with a Kalman Filter (KF), avoiding the need to know aerodynamic models or other aircraft parameters. Assuming that measurements of airspeed and attitude of an UAV are available as inputs, a linear 4th order time-varying model of the UAV's longitudinal speed and the 3-D wind velocity is used to design a Kalman-filter driven by a GNSS velocity measurement airspeed sensor. An observability analysis shows that the states can be estimated along with an airspeed sensor calibration factor provided that the flight maneuvers are persistently exciting, i.e. the aircraft changes attitude. The theoretical analysis of the KF shows that global exponential stability of the estimation error is achieved under these conditions. The method is tested using experimental data from three different UAVs, using their legacy autopilot to provide basic estimates of UAV velocity and attitude. The results show that convergent estimates are achieved with typical flight patterns indicating that excitation resulting from the environment and normal flight operation is sufficient. Wind velocity estimates correlate well with observed winds at the ground. The validation of AOA and SSA estimates is preliminary, but indicate some degree of correlation between the AOA estimate and vertical accelerometer measurements, as would be expected since lift force can be modeled as a linear function of AOA in normal flight.