Wind Estimation Method Research Articles

Ocean Surface Wind Estimation From Waves Based on Small GPS Buoy Observations in a Bay and the Open Ocean

AbstractOcean surface wind and wave information is important in a wide variety of areas, such as coastal disaster reduction, offshore structure design, and atmosphere‐ocean flux estimation. This study proposed a new method for ocean surface wind estimation from surface wave spectrum information measured by small global positioning system buoys. The concept of this method relies on the assumption that the high‐frequency part of the ocean‐wave spectrum is proportional to where is the friction velocity and is the frequency. The determination algorithm for the coefficient of was optimized in this study. The wind direction was determined by the wave cross‐spectrum, assuming that the wind direction aligns the propagation direction of the high‐frequency part of the wave. The proposed wind estimation method was applied to bay and open ocean observations, and the performance of the proposed wind estimation method was similar between the bay and the open ocean. The proposed method improves the wind estimation especially in coastal areas and at high wind speeds in the open ocean compared with the previous method. The performance of the method of the previous study differs between the bay and open ocean due to their spectral shape differences. High‐quality wind and wave information can be obtained using the proposed method. If the mass deployment of small drifting buoys covered the global ocean, the information based on the proposed method could be considerably powerful, and could compensate for the weakness of satellite‐based wind and wave estimations.

Model-Aided Wind Estimation Method for a Tail-Sitter Aircraft

This paper presents a wind estimation method for a dual-rotor tail-sitter unmanned aerial vehicle with all flight phases. The large flight envelope and the slipstream generated by the propellers introduce extra challenges to estimating the wind field during flight for dual-rotor tail-sitter aircraft. In this method, a synthetic wind measurement is proposed based on a low-fidelity aircraft model and operated as a virtual sensor. This synthetic wind measurement and the data from the pitot tube are fused with an extended Kalman filter. The simulation and experimental results of the developed estimation method show a good estimation of the wind speed and direction in the hovering phase, transition, and cruising phases. The proposed wind estimation method was also tested in the hovering phase using the aerodynamic coefficients of NACA 0012 airfoil and a flat plate instead of the vehicle's model to provide a compromise solution for vehicles with no precise aerodynamic model.

A Wind Estimation Method with an Unmanned Rotorcraft for Environmental Monitoring Tasks.

Wind velocity (strength and direction) is an important parameter for unmanned aerial vehicle (UAV)-based environmental monitoring tasks. A novel wind velocity estimation method is proposed for rotorcrafts. Based on an extended state observer, this method derives the wind disturbance from rotors’ speeds and rotorcraft’s acceleration and position. Then the wind disturbance is scaled to calculate the airspeed vector, which is substituted into a wind triangle to obtain the wind velocity. Easy-to-implement methods for calculating the rotorcraft’s thrust and drag coefficient are also proposed, which are important parameters to obtain the wind drag and the airspeed, respectively. Simulations and experiments using a quadrotor in both hovering and flight conditions have validated the proposed method.

A rotor-aerodynamics-based wind estimation method using a quadrotor

Attempts to estimate horizontal wind by the quadrotor are reviewed. Wind estimations are realized by utilizing the quadrotor’s thrust change, which is caused by the wind’s effect on the rotors. The basis of the wind estimation method is the aerodynamic formula for the rotor’s thrust, which is verified and calibrated by experiments. A hardware-in-the-loop simulation (HILS) system was built as a testbed; its dynamic model and control structure are demonstrated. Verification experiments on the HILS system proved that the wind estimation method was effective.

Multi time-step wavefront reconstruction for tomographic adaptive-optics systems.

In tomographic adaptive-optics (AO) systems, errors due to tomographic wavefront reconstruction limit the performance and angular size of the scientific field of view (FoV), where AO correction is effective. We propose a multi time-step tomographic wavefront reconstruction method to reduce the tomographic error by using measurements from both the current and previous time steps simultaneously. We further outline the method to feed the reconstructor with both wind speed and direction of each turbulence layer. An end-to-end numerical simulation, assuming a multi-object AO (MOAO) system on a 30 m aperture telescope, shows that the multi time-step reconstruction increases the Strehl ratio (SR) over a scientific FoV of 10 arc min in diameter by a factor of 1.5-1.8 when compared to the classical tomographic reconstructor, depending on the guide star asterism and with perfect knowledge of wind speeds and directions. We also evaluate the multi time-step reconstruction method and the wind estimation method on the RAVEN demonstrator under laboratory setting conditions. The wind speeds and directions at multiple atmospheric layers are measured successfully in the laboratory experiment by our wind estimation method with errors below 2 ms-1. With these wind estimates, the multi time-step reconstructor increases the SR value by a factor of 1.2-1.5, which is consistent with a prediction from the end-to-end numerical simulation.

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

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.