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

This paper presents two Kalman filter approaches for correcting air data systems providing relative velocity measurements with an additive constant or slowly time-varying bias for fixed-wing unmanned aerial vehicles (UAVs). In addition to the air data system, both estimators rely on a standard sensor suite consisting of a GNSS receiver, an IMU, and a heading reference. Furthermore, the estimators are based on kinematics and do not require a model of the UAV. The two estimators are in the noise-free case proven to have globally exponentially stable (GES) equilibrium points of the error dynamics if provided with persistence of excitation (PE) of the angular velocity of the UAV. The estimators are verified through simulation and using experimental flight data. The relative velocity measurements in the experimental flight results are provided by an array of pressure sensors embedded in the surface of the UAV combined with a neural network algorithm. The results indicate that a certain amount of PE is needed in order to have converging bias estimates for turbulent wind conditions.

[1]  Tor Arne Johansen,et al.  Globally exponentially stable attitude and gyro bias estimation with application to GNSS/INS integration , 2015, Autom..

[2]  B. Anderson.  Stability properties of Kalman-Bucy filters , 1971 .

[3]  Tor Arne Johansen,et al.  Optimal path of a UAV engaged in wind-influenced circular towing , 2017, 2017 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS).

[4]  Tor Arne Johansen,et al.  Estimation of wind velocities and aerodynamic coefficients for UAVs using standard autopilot sensors and a Moving Horizon Estimator , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[5]  Tor A. Johansen,et al.  Approach Methods for Autonomous Precision Aerial Drop from a Small Unmanned Aerial Vehicle , 2017 .

[6]  Stephen A. Whitmore,et al.  Qualitative evaluation of a flush air data system at transonic speeds and high angles of attack , 1987 .

[7]  Young-shin Kang,et al.  Airflow angle and wind estimation using GPS/INS navigation data and airspeed , 2013, 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013).

[8]  Kristoffer Gryte,et al.  Non-linear Model Predictive Control for Longitudinal and Lateral Guidance of a Small Fixed-Wing UAV in Precision Deep Stall Landing , 2016 .

[9]  Florian Nadel,et al.  Stochastic Processes And Filtering Theory , 2016 .

[10]  Tor A. Johansen,et al.  SyncBoard - A high accuracy sensor timing board for UAV payloads , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[11]  Demoz Gebre-Egziabher,et al.  Synthetic Air Data System , 2013 .

[12]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[13]  Stephen A. Whitmore,et al.  Fault-Tolerant Neural Network Algorithm for Flush Air Data Sensing , 1999 .

[14]  Stephen A. Whitmore,et al.  Air Data Sensing from Surface Pressure Measurements Using a Neural Network Method , 1998 .

[15]  Stephen A. Whitmore,et al.  In-flight demonstration of a real-time flush airdata sensing system , 1996 .

[16]  A. Ray,et al.  An introduction to sensor signal validation in redundant measurement systems , 1991, IEEE Control Systems.

[17]  Thor I. Fossen,et al.  On estimation of wind velocity, angle-of-attack and sideslip angle of small UAVs using standard sensors , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[18]  Tor Arne Johansen,et al.  A Machine Learning Approach for Estimating Air Data Parameters of Small Fixed-Wing UAVs Using Distributed Pressure Sensors , 2020, IEEE Transactions on Aerospace and Electronic Systems.

[19]  J. Neidhoefer,et al.  Wind Field Estimation for Small Unmanned Aerial Vehicles , 2010 .

[20]  T. J. Larson,et al.  Subsonic tests of an all-flush-pressure-orifice air data system , 1981 .

[21]  Christophe Rabut,et al.  Wind-optimal path planning: Application to aircraft trajectories , 2014, 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV).

[22]  Shujie Song,et al.  Method of Estimating Angle-of-Attack and Sideslip Angel Based on Data Fusion , 2009, 2009 Second International Conference on Intelligent Computation Technology and Automation.

[23]  Hemendra Arya,et al.  Multistage-Fusion Algorithm for Estimation of Aerodynamic Angles in Mini Aerial Vehicle , 2012 .

[24]  Mogens Blanke,et al.  Diagnosis of Wing Icing Through Lift and Drag Coefficient Change Detection for Small Unmanned Aircraft , 2015 .

[25]  Ian Postlethwaite,et al.  Neural-network-based flush air data sensing system demonstrated on a mini air vehicle , 2010 .

[26]  Marcello R. Napolitano,et al.  Aircraft model-independent airspeed estimation without pitot tube measurements , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[27]  J. Meyer,et al.  Generalized Inverses and Ranks of Block Matrices , 1973 .

[28]  M. Tartagni,et al.  Aircraft angle of attack and air speed detection by redundant strip pressure sensors , 2004, Proceedings of IEEE Sensors, 2004..

[29]  Tor Arne Johansen,et al.  Combining model-free and model-based angle of attack estimation for small fixed-wing UAVs using a standard sensor suite , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[30]  Timothy W. McLain,et al.  Vector Field Path Following for Miniature Air Vehicles , 2007, IEEE Transactions on Robotics.

[31]  Jack W. Langelaan,et al.  Flush Air Data Sensing for Soaring-Capable UAVs , 2013 .