Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter

  • Lee, Han-Sung (School of Electrical Engineering, Seoul National University) ;
  • Ra, Won-Sang (School of Mechanical and Control Engineering, Handong Global University) ;
  • Lee, Jang-Gyu (School of Electrical Engineering, Seoul National University) ;
  • Song, Yong-Kyu (School of Aerospace and Mechanical Engineering, Korea Aerospace University) ;
  • Whang, Ick-Ho (Department of Guidance and Control, Agency for Defense Development)
  • Received : 2011.04.12
  • Accepted : 2011.05.25
  • Published : 2012.01.01


A non-conservative robust Kalman filter (NCRKF) is applied to flight data to identify the aerodynamic derivatives of an unmanned autonomous vehicle (UAV). The NCRKF is formulated using UAV lateral motion data and then compared with results from the conventional Kalman filter (KF) and the recursive least square (RLS) method. A superior performance for the NCRKF is demonstrated by simulation and real flight data. The NCRKF is especially effective in large uncertainties in vehicle modeling and in measuring flight data. Thus, it is expected to be useful in missile and aircraft parameter identification.


Supported by : Agency for Defense Development


  1. Brain L. Stevens, Frank L. Lewis, Aircraft Control and Simulation. John Wiley and Sons, Inc., 2003, ch. 2.
  2. P. G. Hamel and R. Jategaonkar, "Evolution of flight vehicle system identification," J. Aircraft, 1996, 33, (1), pp. 9-28.
  3. M. R. Ananthasayanam, "Parameter Estimation of a Flight Vehicle Using MMLE/BFGS Estimator Under Limited Measurements," AIAA Paper 02-4624, August, 2002.
  4. T. J. Sooy, "Aerodynamic Predictions, Comparisons, and Validations Using Missile DATCOM (97) and Aeroprediction 98 (AP98) ," AIAA Paper 04-1246, January, 2004.
  5. G. Chowdhary, "Aerodynamic Parameter Estimation from Flight Data Applying Extended and Unscented Kalman Filter," AIAA Paper 06-6146, August, 2006.
  6. S. Singh, "Parameter Estimation from Flight Data of a missile using Maximum Likelihood and Neural Network Method," AIAA Paper 06-6284, August, 2006.
  7. Y. Song and B. Song, "A Comparative Study of Real-Time Aircraft Parameter Identification Schemes Applied to NASA F/A-18 HARV Flight Data," Transactions of the Japan Society for Aeronautical and Space Sciences, Vol. 45, No. 149, 2002.
  8. Y. Song, B. Seanor, M. Napolitano, G. Campa, "Online Parameter Estimation Techniques Comparison Within a Fault Tolerant Flight Control System," Journal of Guidance, Control, and Dynamics, Vol. 25, No. 3, 2002
  9. J.G. Lee and D. H. Lee, "Aerodynamic Parameter Identification of a Tactical Missile Utilizing Post flight Telemetry Data," AIAA 12th Atmospheric Flight Mechanics Conference, Snowmass, Colorado, August 1985.
  10. H. Y. Yang, Aerodynamic Parameter Identification of a Missile using Maximum Likelihood Method and Extended Kalman Filter, MS. Dissertation, Department of Electrical Engineering and The Graduate School of Seoul National University, 1984.
  11. T. G. Sung, Missile Aerodynamic Parameter Identification under Uncertain Model Structure, MS. Dissertation, Department of Electrical Engineering and The Graduate School of Seoul National University, 1986.
  12. D. H. Lee, Comparison of Parameter Identification Algorithms for an Aircraft, Ph.D. Dissertation, Department of Electrical Engineering and The Graduate School of Seoul National University, 1992.
  13. S. T. Park, J.G. Lee and G. Chen, "Comments on 'Modified Extended Kalman Filtering and a Real-Time Parallel Algorithm for System Parameter Identification," IEEE Transactions on Automatic Control, Vol. 40, No. 9, September 1995, pp. 1661-1662.
  14. Y. Song and M.S. Hwang, "A Study on the Aircraft Parameter Estimation form Flight Test Data," Journal of the KSAS, Vol. 26, No. 6, October, 1998, pp. 1-12.
  15. W. S. Ra, I. H. Whang, and J. B. Park, "Non-conservative robust Kalman filtering using a noise corrupted measurement matrix", IET Control Theory and Applications, 2009, Vol. 3, Iss. 9, pp. 1226-1236.
  16. W. S. Ra, Non conservative Robust Kalman Filtering Using Noise Corrupted Measurement Matrix, Ph.D. Dissertation, Department of Electrical and Electronic Engineering and The Graduate School of Younsei University, December 2008.
  17. I. H. Whang, W. S. Ra, and J. Y. Ahn, "A modified weighted least squares range estimator for ASM (anti-ship missile) application," Int. Journal of Control, Automation, and Systems, 2005, 3, (3), pp. 486-492.
  18. Robert C. Nelson, Flight Stability and Automatic Control, 2nd Ed., McGraw Hill, 1998.
  19. Simon Haykin, Adaptive filter theory, Prentice Hall, Upper Saddle River, New Jersey 070458, 2002.
  20. A. Gelb (ed.), Applied optimal estimation, Cambridge, MA: MIT press, 1974.
  21. F. L. Lewis, Optimal estimation: with an introduction to stochastic control theory, New York: John Wiley & Sons, 1986.

Cited by

  1. System identification of an airship using trust region reflective least squares algorithm vol.15, pp.3, 2017,
  2. Performance Degradation Due to Particle Impoverishment in Particle Filtering vol.9, pp.6, 2014,
  3. A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics vol.8, pp.6, 2013,