• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.859-866
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• 2009

The article discusses an issue of estimating the airspeed of an autonomous flying vehicle. Airspeed is the difference between ground speed and wind speed. It is desirable to know any two among the three speeds for navigation, guidance and control of an autonomous vehicle. For example, ground speed and position are used to guide a vehicle to a target point and wind speed and airspeed are used to maximize flight performance such as a gliding range. However, the target vehicle has not an airspeed sensor but a ground speed sensor (GPS/INS). So airspeed or wind speed has to be estimated. Here, airspeed is to be estimated. A vehicle's dynamics and its dynamic parameters are used to estimate airspeed with attitude and angular speed measurements. Kalman filter is used for the estimation. There are also two major sources arousing a robust estimation problem; wind speed and altitude. Wind speed and direction depend on weather conditions. Altitude changes as a vehicle glides down to the ground. For one reference altitude, multiple model Kalman filters are pre-designed based on several reference airspeeds. We call this group of filters as a cluster. Filters of a cluster are activated simultaneously and probabilities are calculated for each filter. The probability indicates how much a filter matches with measurements. The final airspeed estimate is calculated by summing all estimates multiplied by probabilities. As a vehicle glides down to the ground, other clusters that have been designed based on other reference altitudes are activated. Some numerical simulations verify that the proposed method is effective to estimate airspeed.

• International Journal of Aeronautical and Space Sciences
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• v.18 no.2
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• pp.344-351
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• 2017

This paper presents a method to estimate steady wind and airspeed bias error using an aircraft with GPS and airspeed sensor. The estimation uses the vector relation between the inertial, air, and wind velocities through a novel design of an extended Kalman filter. The observability analysis is also presented to show that the aircraft is required to keep changing its flight direction for the desired observability. The feasibility and performance of the proposed algorithm is demonstrated through simulations and flight experiments.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.5
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• pp.405-412
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• 2015

This paper represents a filter design to estimate the airspeed of a spin-stabilized, trajectory-correctible artillery ammunition. Due to the limited power and space in operational point of view, the airspeed sensor is not installed, and thus the airspeed need to be estimated using limited sensor measurements. The only IMU measurements(three-axis specific forces and angular rates) are used in this application. The extended Kalman filter algorithm is applied since a linear filter can not cover the its wide operational range in airspeed and altitude. In the implementation of the EKF, the state and measurement equations are transformed into the no-roll frame for simple form of Jacobian matrix. The simulation study is conducted to evaluate the performance of the filter under various environment conditions of sensor noise and wind turbulence. In addition, the effect of the choice in filter design parameters, i.e. process error covariance matrices is analyzed on the performance of the estimation of airspeed and angular rates.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.1
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• pp.67-74
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• 2022

To analyze the motion of aircraft through computing the dynamics equations, true airspeed is essential for obtaining aerodynamic loads. Although the airspeed is measured by on-board instruments such as pitot tubes, measurement data are difficult to obtain for commercial flights because they include sensitive data about the airline operations. One of the commonly available trajectory data, Automatic Dependent Surveillance-Broadcast data, provide aircraft's speed in the form of ground speed. The ground speed is a vector sum of the local wind velocity and the true airspeed. This paper present a method to estimate true airspeed by combining the trajectory, meteorological, and topology data available to the public. To integrate each data, we first matched the coordinate system and then unified the altitude reference to the mean sea level. We calculated the wind vector for all trajectory points by interpolating from the lower resolution grid of the meteorological data. Finally, we calculate the true airspeed from the ground speed and the wind vector. These processes were applied to several sample trajectories with corresponding meteorological data and the topology data, and the estimated true airspeeds are presented.

• Journal of Aerospace System Engineering
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• v.3 no.1
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• pp.1-5
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• 2009

The flight control of the T-50 advanced trainer is conducted by the digital FBW (Flight-by-Wire) control system. The system input data consist of flight conditions such as altitude, airspeed, and angle of attack. And the flight conditions of the aircraft are obtained from IMFP (Integrated Multi-Function Probe). The T-50 aircraft equip three IMFP sensors. To ensure reliability in flight condition data obtained from each IMFP sensor, the mean value of flight conditions is used as the input of the control system. In this study, the effect of an installation angle of IMFP sensors on estimation of flight altitude was investigated by flight test results in the supersonic region.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.46 no.6
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• pp.464-470
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• 2018

In this paper, a practical method of estimating the flap hinge moments which change according to the aircraft flap operations was introduced. For the flap design, the hinge moment derived by structural load analysis and wind tunnel tests was able to be compared with the real flight hinge moment, and the static safety of the flap structure could be verified though this comparison. In order to perform the tests, two strain gauges were installed on the flap hinge and an onboard device for aircraft load monitoring was utilized. Through the ground test, the correlation between the strain and the moment of the flap hinge was calibrated with analytic and finite element analysis. During the flight test, strain signals together with the flap deflection angles and airspeed were recorded. Finally, the flight hinge moments could be predicted by the measured strain which was calibrated with the analytic and the finite element analysis.