|
Vol: 59(73) No: 1 / June 2014 GPS and IMU Based State Estimation Method for Aircraft INS Navigation Loránd Lukács Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar Tudósok krt. 2, H-1117 Budapest Hungary, e-mail: lukacsl@iit.bme.hu Béla Lantos Department of Control Engineering and Information Technology, Budapest University of Technology and Economics, Magyar Tudósok krt. 2, H-1117 Budapest Hungary, e-mail: lantos@iit.bme.hu Keywords: Aircraft identification, Data fusion, State estimation, Extended Kalman filtering, Angle of attack, Sideslip angle Abstract The study presents a state estimation method for the determination of position, speed, spatial orientation and angular velocity of an aircraft based on the data fusion using Extended Kalman Filtering and actual flight data. The sensors mounted on the aircraft include GPS, 3D accelerometer, angular velocity and magnetometer. The paper also proposes a method for determining the angle of attack and sideslip angles of the aircraft. The state variables together with actuator signals (determined separately) are considered as input signals for the identification of the nonlinear model of an aircraft. The achieved results can be used for long range inertial navigational systems (INS) or aircraft system identification with the knowledge of the control signals. References [1] V. Klein and E. Morelli, Aircraft System Identification, Theory and Practice. American Institute of Aeronautics and Astronautics, 2006. [2] J. Farrel, GPS with High Rate Sensors. New-York: McGraw-Hill, 2008. [3] L. Lukács and B. Lantos, “Data Fusion and Primary Image Processing for Aircraft Identification,” Periodica Polytechnica, Electrical Engineering and Computer Science, vol. 56, no. 3, pp. 83–94, 2012. [4] K.-P. Schwarz and M. Wei, “INS/GPS Integration for Geodetic Applications: lecture Notes ENGO 623,” Research report, Dept. of Geomatics Eng., The University of Calgary, Calgary, Canada 2000. [5] J. Farrel and M. Barth, The Global Positioning System and Inertial Navigation. New York: McGraw-Hill, 1999. [6] B. Lantos and L. Márton, Nonlinear Control of Vehicles and Robots. London: Springer, 2011. [7] B. Lantos, Irányítási rendszerek elmélete és tervezése II. Budapest: Akadémiai kiadó, 2003. [8] J. Farrel, Aided Navigation. GPS with High Rate Sensors. New-York: McGraw-Hill, 2008. |