Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-05T16:15:02.785Z Has data issue: false hasContentIssue false
  • English
  • Français

Multi sensor data fusion based approach for the calibration of airdata systems

Published online by Cambridge University Press:  27 January 2016

M. Majeed  and
I. N. Kar
M. Majeed*
Affiliation:
Flight Mechanics and Control Division, CSIR - National Aerospace Laboratories, Bangalore, India
I. N. Kar*
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology, Delhi, India
*
[email protected]
[email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Accurate and reliable airdata systems are critical for aircraft flight control system. In this paper, both extended Kalman filter (EKF) and unscented Kalman filter (UKF) based various multi sensor data fusion methods are applied to dynamic manoeuvres with rapid variations in the aircraft motion to calibrate the angle-of-attack (AOA) and angle-of-sideslip (AOSS) and are compared. The main goal of the investigations reported is to obtain online accurate flow angles from the measured vane deflection and differential pressures from probes sensitive to flow angles even in the adverse effect of wind or turbulence. The proposed algorithms are applied to both simulated as well as flight test data. Investigations are initially made using simulated flight data that include external winds and turbulence effects. When performance of the sensor fusion methods based on both EKF and UKF are compared, UKF is found to be better. The same procedures are then applied to flight test data of a high performance fighter aircraft. The results are verified with results obtained using proven an offline method, namely, output error method (OEM) for flight-path reconstruction (FPR) using ESTIMA software package. The consistently good results obtained using sensor data fusion approaches proposed in this paper establish that these approaches are of great value for online implementations.

Type
Research Article
Information
The Aeronautical Journal , Volume 115 , Issue 1164 , February 2011 , pp. 113 - 122
Copyright
Copyright © Royal Aeronautical Society 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Menzies, M.A. Integrated air data sensors, Aeronaut J, 105, (1046), April 2001, pp 223–229.CrossRefGoogle Scholar
2. Parameswaran, V, Jategaonkar, R.V. and Press, M. Five-hole flow angle probe calibration from dynamic and tower flyby maneuvers, J Aircr, 42, (1), January–February 2005.CrossRefGoogle Scholar
3. Edward, A and Haering, . Air data measurement and calibration, December 1995, NASA-TM 104316.Google Scholar
4. Gonsalez, J.C. and Arrington, E.A. Five-hole flow angle calibration for the NASA Glenn icing research tunnel, NASA/CR-1999-202330, AIAA–96–2201.Google Scholar
5. Stephen, A. and Terry, J. High angle-of-attack flush airdata sensing system, J Aircr, 1992, 29, (5), pp 915–921.Google Scholar
6. Lando, M., Manuela, and Piero, , Neuro-fuzzy techniques for the air-data sensor calibration, J Aircr, 44, (3), May-June 2007.CrossRefGoogle Scholar
7. Chu, Q.P., Mulder, JA. and Van Woerkom, P.T.L.M. Modified recursive maximum likelihood adaptive filter for nonlinear aircraft flight-path reconstruction, AIAA J, Guidance Control Dyn, 1996; 19, (6), pp 1285-95.Google Scholar
8. Klein, V. and Schiess, J.R. Compatibility check of measured aircraft responses using kinematic equations and extended Kalman filter, August 1977, NASA TN D-8514.Google Scholar
9. Jategaonkar, R.V. Identification of the aerodynamic models of the DLR research aircraft ATTAS from flight test data, July 1990, DLR, German Aerospace Center, Rept DLR-FB 90-40, Brunswick, Germany.Google Scholar
10. Keskar, D.A. and Klein, V. Determination of instrumentation errors from measured data using maximum likelihood method, 1980, AIAA 80-1602.CrossRefGoogle Scholar
11. Maine, R.E., and Iliff, K.W. Identification of dynamic systems applications to aircraft Part 1: The output error approach, December 1986, AG-300, AGARD, 3, (1).Google Scholar
12. Laban, M.,On-line Aircraft Aerodynamic Model Identification, 1994, PhD Dissertation, Delft University of Technology, Delft, Netherlands.Google Scholar
13. De Braga, C.M., Moreina, E.H. and Carlos, L.S.G. Adaptive stochastic filtering for online aircraft flight path reconstruction, J Aircr, September-October 2007, 44, (5).Google Scholar
14. Fitzgerald, R.J. Divergence of the Kalman fFilter, IEEE Transactions on Automatic Contro, 1971, AC-16, (6), pp 736–747.CrossRefGoogle Scholar
15. Julier, S. and Uhlmann, J.K. Unscented filtering and nonlinear estimation, Proceedings of the IEEE, March 2004, 92, pp 401–422.CrossRefGoogle Scholar
16. Wise, K.A. Flight testing of the X-45 A J-UCAS computational AlphaBeta system, 2006, AIAA 2006-6215.CrossRefGoogle Scholar
17. Hall, D.L. Mathematical Techniques in Multi Sensor Data Fusion, 1992, 1st edition, Artech House, Norwood.Google Scholar
18. Bar-Shalom, Y. and Fortmann, T.E. Tracking and Data Association, 1998, Academic Press, New York.Google Scholar
19. Saha, R.K. Track-to-track fusion with dissimilar sensors, IEEE Transactions on Aerospace and Electronic Systems, 1996, 34, (3), pp 1021–1029.CrossRefGoogle Scholar
20. Hui, K., Srinivasan, R. and Baillie, S. Simultaneous calibration of aircraft position error and airflow angles using differential GPS, Canadian Aeronautics and Space J, December 1996, 42, (4), pp 185–193.Google Scholar
21. Jategaonkar, R.V. ESTIMA — a modular and integrated software tool for parameter estimation and simulation, July 2001, DLR, German Aerospace Center, IB 111-2001/29, Brunswick, Germany.Google Scholar
22. Hamel, P.G. and Jategaonkar, R.V. Evolution of flight vehicle system identification, J Aircr, 1996, 33, (1), pp 9–28.CrossRefGoogle Scholar
23. Maybeck, P.S. Stochastic Models, Estimation, and Control, Vol 1, 1979, Academic Press, New York.Google Scholar
24. Jategaonkar, R.V. Flight vehicle system identification: A time domain methodology, August 2006, 216, AIAA Progress in Astronautics and Aeronautics Series, AIAA, Reston, VA.CrossRefGoogle Scholar
25. Gan, Q. and Harris, C.J. Comparison of two measurement fusion methods for Kalman-filter-based multi sensor data fusion, IEEE Transactions on Aerospace and Electronic Systems, January 2001, 37, (1).CrossRefGoogle Scholar
26. Roecker, J.A. and Mcgillem, C.D. Comparison of two sensor tracking methods based on state-vector fusion and measurement fusion, IEEE Transactions on Aerospace and Electronic Systems, 24, (4), 1988, pp 447–449.CrossRefGoogle Scholar
27. Chang, K.C., Saha, R.K. and Bar-Shalom, Y. On optimal track-to-track fusion, IEEE Transactions on Aerospace and Electronic Systems, 1997, 33, (4), pp 1271–1276.CrossRefGoogle Scholar
28. Saha, R.K. Effect of common process noise on two-track fusion, J Guidance, Control Dynamics, 1996, 19, pp 825–835.CrossRefGoogle Scholar
29. Marvin, J.G. A perspective on CFD validation, 1993, Dryden Lectureship in Research, AIAA-1993-2, 31st Aerospace Sciences Meeting, 11-14 January 1993, Reno, NV.Google Scholar