Kalman Filters for Air Data System Bias Correction for a Fixed-Wing UAV

Borup, Kasper Trolle; Stovner, Bård Nagy; Fossen, Thor I.; Johansen, Tor Arne

doi:10.1109/tcst.2019.2931672

Cited by 21 publications

(7 citation statements)

References 30 publications

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“…In [7], Kalman filters (KF) were developed for wind velocity estimation. The convergence rate for the KF is very low, and the accuracy is limited.…”

Section: A Review Of Related Workmentioning

confidence: 99%

Frequency-Based Wind Gust Estimation for Quadrotors Using a Nonlinear Disturbance Observer

Asignacion

Suzuki

Noda

et al. 2022

IEEE Robot. Autom. Lett.

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In city-wide weather prediction, wind gust information can be obtained using unmanned aerial vehicles (UAVs). Although wind sensors are available, an algorithm-based active estimation can be helpful not only as a weightless substitute but also as feedback for robust control. This paper aims to estimate the wind gusts affecting the quadrotors (a type of UAV) as the input disturbances by using a frequency-based nonlinear disturbance observer (NDOB). To obtain highly accurate estimations, frequency is considered as the main design parameter, thereby focusing the estimation on the frequency range of the wind gusts. The NDOB is developed using the Takagi-Sugeno (T-S) fuzzy framework. In this approach, the twelfth-order nonlinear model is approximated into a sixth-order T-S fuzzy model to reduce computational cost. A two-step verification method is presented, which includes MATLAB/Simulink simulations and the experiments performed using a 2.5 kg quadrotor.

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“…In [7], Kalman filters (KF) were developed for wind velocity estimation. The convergence rate for the KF is very low, and the accuracy is limited.…”

Section: A Review Of Related Workmentioning

confidence: 99%

Frequency-Based Wind Gust Estimation for Quadrotors Using a Nonlinear Disturbance Observer

Asignacion

Suzuki

Noda

et al. 2022

IEEE Robot. Autom. Lett.

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“…It serves as a vital indicator for UAV control, enabling functions such as navigation, positioning, flight performance monitoring, maintaining flight stability, and enhancing flight safety. Common methods for measuring airspeed in UAVs include Pitot tube velocity measurements [1][2][3][4] and integrated Global Positioning System (GPSs) with low-cost Inertial Measurement Unit (IMU) [5][6][7][8] . For small UAVs with lower cost and less stringent airspeed measurement requirements, GPS and IMU strategies can be expensive.…”

Section: Introductionmentioning

confidence: 99%

Designing of Airspeed Measurement Method for UAV Based on MEMS Pressure Sensors

chen,

Li,

et al. 2024

Preprint

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Airspeed measurement is crucial for UAV control. To achieve accurate airspeed measurements for UAV, this paper calculates airspeed data by measuring changes in air pressure and temperature. Based on this, a data processing method based on mechanical filtering and improved AR-SHAKF algorithm is proposed to indirectly measure airspeed with high precision. In particular, a mathematical model for an airspeed measurement system is established, and an installation method for the pressure sensor is designed to measure the total pressure, static pressure and temperature. Secondly, the measurement principle of the sensor are analyzed, and a metal tube is installed to act as a mechanical filter, particularly in cases where the aircraft has a significant impact on the gas flow field. Furthermore, a time-series model is used to establish the sensor state equation and the initial noise values. It also enhances the Sage-Husa adaptive filter to analyze the unavoidable error impact of initial noise values. By constraining the range of measurement noise, it achieves adaptive noise estimation. To validate the superiority of the proposed method, a low-complexity airspeed measurement device based on MEMS pressure sensors is designed. The results demonstrate that the airspeed measurement device and the designed velocity measurement method can effectively calculate airspeed with high measurement accuracy and strong interference resistance.

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“…In the middle phase of UAV flight, the flight state is controlled based on the flight speed, specifically the airspeed value. Common methods for measuring the UAV airspeed include a pitot tube velocity measurement [ 15 , 16 , 17 , 18 ], integrated GPS with low-cost inertial navigation systems (IMU), and others [ 19 , 20 , 21 , 22 ]. For UAVs where precision in airspeed measurement is not crucial, the high cost of integrating GPS with IMU can be prohibitive.…”

Section: Introductionmentioning

confidence: 99%

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

Chen,

Li,

et al. 2024

Sensors

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Flight parameters are crucial criteria for UAV control, playing a significant role in ensuring the safe and efficient completion of missions. Launch force and airspeed information are key parameters in the early and middle stages of flight, serving as important data for monitoring the UAV’s flight status. In response to challenges such as weak launch force, low identification rates, small airspeed, and low recognition accuracy in UAVs, a method for identifying UAV flight parameters based on launch force and airspeed is proposed. From the aspect of launch force identification, a recognition method based on a low-g value accelerometer information source is proposed, utilizing a ‘multi-level time window + threshold’ approach. For airspeed identification, an optimization method for airspeed measurement under the Kalman filter architecture is introduced. A device for airspeed measurement based on pressure sensors is designed, and the recommended installation position is determined through simulation. Furthermore, the feasibility and robustness of the proposed launch force identification and airspeed measurement optimization methods are validated through simulation. Finally, the effectiveness of the design is verified through centrifuge and wind tunnel experiments. This research provides technical support for the identification of the launch force and airspeed measurement in UAVs.

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Kalman Filters for Air Data System Bias Correction for a Fixed-Wing UAV

Cited by 21 publications

References 30 publications

Frequency-Based Wind Gust Estimation for Quadrotors Using a Nonlinear Disturbance Observer

Frequency-Based Wind Gust Estimation for Quadrotors Using a Nonlinear Disturbance Observer

Designing of Airspeed Measurement Method for UAV Based on MEMS Pressure Sensors

Research on UAV Flight Parameter Identification Method Based on Launch Force and Airspeed

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