UAV Attitude, Heading, and Wind Estimation Using GPS/INS and an Air Data System

A new attitude, heading, and wind estimation algorithm is proposed, which incorporates measurements from an air data system to properly relate predicted attitude information with aircraft velocity information. Experimental Unmanned Aerial Vehicle (UAV) flight data was used to validate the proposed approach. The experimental results demonstrated effective estimation of the roll, pitch, yaw, and heading angles, and provided a smoothed estimate of the angle of attack and sideslip angles. The wind estimation results were validated with respect to measurments provided by a local weather station. It was shown that this new method of attitude estimation is effective in distinguishing the yaw and heading angles of the aircraft, properly regulating the attitude estimates with air data system measurements, and provding a reasonable estimate of the local wind field.

[1]  Zhiqiang Xing,et al.  Over-bounding Integrated INS/GNSS Output Errors , 2010 .

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

[3]  Jihoon Kim,et al.  Wind Estimation and Airspeed Calibration using a UAV with a Single-Antenna GPS Receiver and Pitot Tube , 2011, IEEE Transactions on Aerospace and Electronic Systems.

[4]  C. Lefas Real-Time Wind Estimation and Tracking with Transponder Downlinked Airspeed and Heading Data , 1987, IEEE Transactions on Aerospace and Electronic Systems.

[5]  Frank L. Lewis,et al.  Optimal Control , 1986 .

[6]  Marcello R. Napolitano,et al.  Evaluation of Matrix Square Root Operations for UKF within a UAV GPS/INS Sensor Fusion Application , 2011 .

[7]  Marcello R. Napolitano,et al.  Sensitivity Analysis of Extended and Unscented Kalman Filters for Attitude Estimation , 2013, J. Aerosp. Inf. Syst..

[8]  Anthony J. Calise,et al.  Nonlinear adaptive flight control using neural networks , 1998 .

[9]  Makoto Kumon,et al.  Wind Estimation by Unmanned Air Vehicle with Delta Wing , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[10]  J.L. Crassidis,et al.  Sigma-point Kalman filtering for integrated GPS and inertial navigation , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[11]  Eugene A. Morelli,et al.  Aircraft system identification : theory and practice , 2006 .

[12]  F. Tung,et al.  Navigation and control. , 1968 .

[13]  Marcello R. Napolitano,et al.  Flight-Test Evaluation of Sensor Fusion Algorithms for Attitude Estimation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[14]  D. W. Allan,et al.  Statistics of atomic frequency standards , 1966 .

[15]  Shen Li,et al.  The research on unmanned aerial vehicle remote sensing and its applications , 2010, 2010 2nd International Conference on Advanced Computer Control.

[16]  Zhiqiang Xing,et al.  Modeling and bounding low cost inertial sensor errors , 2008, 2008 IEEE/ION Position, Location and Navigation Symposium.

[17]  R. E. Kalman,et al.  New Results in Linear Filtering and Prediction Theory , 1961 .

[18]  Costanzo Manes,et al.  Comparative Study of Unscented Kalman Filter and Extended Kalman Filter for Position/Attitude Estimation in Unmanned Aerial Vehicles , 2008 .

[19]  Marcello R. Napolitano,et al.  On-line Modeling and Calibration of Low-Cost Navigation Sensors , 2011 .

[20]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.