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Aircraft parameter identification using an estimation-before-modelling technique

Published online by Cambridge University Press:  04 July 2016

J. C. Hoff
Affiliation:
College of Aeronautics Cranfield University Cranfield Bedford UK
M. V. Cook
Affiliation:
College of Aeronautics Cranfield University Cranfield Bedford UK

Abstract

This paper describes a comparative evaluation of two data smoothing algorithms for use in a two step estimation-beforemodelling procedure for aircraft parameter identification. A simple fixed lag smoother is compared with the usual, and more complex, modified-Bryson-Frazier smoother in the first, state estimation, step of the aircraft parameter identification procedure. The comparison is illustrated by application to the analysis of the Dutch Roll motion of the Embraer EMB-312 Tucano. Both algorithms were found to give results of comparable accuracy although the fixed lag smoother is computationally more efficient. It was concluded that the fixed lag smoother algorithm is an acceptable alternative to the modified-Bryson-Frazier algorithm in aircraft parameter identification applications.

Type
Research Article
Copyright
Copyright © Royal Aeronautical Society 1996 

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References

1. Fioretti, S. and Jetto, L. Low a priori information model for optimal smoothing and differentiation of noisy signals, Int J Adaptive Control and Signal Processing, 8, 1994, John Wiley & Sons.Google Scholar
2. Bierman, G. Fixed interval smoothing with discrete measurements, Int J Control, 1973,18,(1).Google Scholar
3. Kay, S. Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall, 1993.Google Scholar
4. Gelb, A. Applied Optimal Filtering, MIT Press, Cambridge Mass, 1974.Google Scholar
5. Bauer, J and Andrisani, D. Estimating short period dynamics using an extended Kalman filter, AIAA Conference Proceedings paper, AIAA-90-1277-CP, 1990.Google Scholar
6. Klein, V. and Schiess, J. Compatibility check of measured aircraft responses using kinematic equation and extended Kalman filter, NASA TND-8514, 1977.Google Scholar
7. Moek, G. Some Applications of Estimation Theory, PhD Dissertation, Delft University of Technology. Also, Report NLR TP-91100- U, National Aerospace Laboratory, The Netherlands, 1991.Google Scholar
8. Draper, N.R. and Smith, H. Applied Regression Analysis, John Wiley & Sons, 1966.Google Scholar
9. Moore, J.B. and Anderson, B. Optimal Filtering, Prentice Hall, 1979.Google Scholar
10. Hoff, J.C. Aircraft Parameter Estimation by Estimation-Before-Modelling Technique, PhD thesis, Cranfield University, November 1995.Google Scholar