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Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter
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 Title & Authors
Aerodynamic Derivatives Identification Using a Non-Conservative Robust Kalman Filter
Lee, Han-Sung; Ra, Won-Sang; Lee, Jang-Gyu; Song, Yong-Kyu; Whang, Ick-Ho;
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 Abstract
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.
 Keywords
Non-conservative robust Kalman filter;Aerodynamic derivatives identification;EKF;RLS;
 Language
English
 Cited by
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A Target Tracking Based on Bearing and Range Measurement With Unknown Noise Statistics, Journal of Electrical Engineering and Technology, 2013, 8, 6, 1520  crossref(new windwow)
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Performance Degradation Due to Particle Impoverishment in Particle Filtering, Journal of Electrical Engineering and Technology, 2014, 9, 6, 2107  crossref(new windwow)
 References
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. crossref(new window)

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. crossref(new window)

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. crossref(new window)

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.