Hostname: page-component-89b8bd64d-shngb Total loading time: 0 Render date: 2026-05-10T13:30:56.106Z Has data issue: false hasContentIssue false
  • English
  • Français

Aircraft parameter identification using anestimation-before-modellingtechnique

Published online by Cambridge University Press:  04 July 2016

J. C. Hoff  and
M. V. Cook
J. C. Hoff
Affiliation:
College of Aeronautics Cranfield University Cranfield Bedford UK
M. V. Cook
Affiliation:
College of Aeronautics Cranfield University Cranfield Bedford UK
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper describes a comparative evaluation of twodata smoothing algorithms for use in a two stepestimation-beforemodelling procedure for aircraftparameter identification. A simple fixed lagsmoother is compared with the usual, and morecomplex, modified-Bryson-Frazier smoother in thefirst, state estimation, step of the aircraftparameter identification procedure. The comparisonis illustrated by application to the analysis of theDutch Roll motion of the Embraer EMB-312 Tucano.Both algorithms were found to give results ofcomparable accuracy although the fixed lag smootheris computationally more efficient. It was concludedthat the fixed lag smoother algorithm is anacceptable alternative to themodified-Bryson-Frazier algorithm in aircraftparameter identification applications.

Information

Type
Research Article
Information
The Aeronautical Journal , Volume 100 , Issue 997 , September 1996 , pp. 259 - 268
Copyright
Copyright © Royal Aeronautical Society 1996 

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.)

Article purchase

Temporarily unavailable

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