Real-Time Wind Field Estimation and Pitot Tube Calibration Using an Extended Kalman Filter

Abstract

The airspeed is an important feedback signal for flight control, and its measurement accuracy is related to the safety of aircraft, especially for hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAV) in the transition phase. However, offline calibration of the pitot tube cannot fully simulate the situation in real cases, and this is why online calibration after installation is necessary. In addition, the environmental wind field creates a high risk for the conversion flight of a hybrid UAV, thus real-time wind field measurement of the flight field has great significance for the flight path planning of the conversion process. In this article, an innovative method for online calibration of pitot tube and wind field estimation is proposed. After establishing the extended Kalman filter (EKF) for estimation, an analysis method is proposed to analyze the observability of EKF under different flight strategies. Then, the hovering flight is selected as the experimental flight trajectory. The laboratory computer simulation and flight experiment results validate the theory and prove that the proposed method could re-calibrate the scale factor of the pitot tube and estimate the wind field.

Estimation of Airspeed, Angle of Attack, and Sideslip for Small Unmanned Aerial Vehicles (UAVs) Using a Micro-Pitot Tube

“…As shown in Table 6, the relative error that affected airspeed measurements was found to be less than 9% in all the test scenarios considered, or less than 5% if we excluded test case 4 shown in Table 6, relative to a nonrealistic value of the AOA (which was typically in the range of −2 • to 15 • ). The estimation errors of α, β, and V CAS were found to be within 1.7 • (excluding the nonrealistic case α = 45 • ), 0.5 • , and 1.4 m/s, respectively, in good agreement with the theoretical values derived from the law of error propagation, and consistent with other authors' work [15,19,20,26]. The proposed approach showed a promising potentiality for implementation of real-time control laws to increase the flight envelope by exploiting attitude measurements and direct knowledge of α and β.…”

“…The sensors used in this research are a differential pressure sensor and a 10 degrees of freedom (DoF) inertial measurement unit (IMU), both managed by a microcontroller Arduino for data acquisition and TAS, AOA, and AOS estimation. We used 1D Kalman filtering to estimate and remove measurement noise, and complementary filtering was used to provide an estimate of the attitude from IMU acceleration and angular rate data [25,26]. Indoor sessions were needed for system setup and calibration.…”

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

“…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.…”

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

“…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.…”