Aircraft Trajectory Tracking and Prediction

Abstract

Regression modelling of trajectory measurement data was examined as a means for improving the performance of aircraft trajectory tracking and prediction. Regression models were used for adaptively removing measurement noise from trajectory observations and extrapolating trajectory measurements. A comparative study was done between three models of aircraft dynamics used in an extended Kalman filter: a strictly translational model, an attitude/translation model, and an attitude/translation model that uses vehicle specific inertial characteristics. Adaptive regression models were used for measurement accuracy enhancement. Comparisons were also made between errors resulting from position and attitude predictions using Runge-Kutta integration and extrapolated regression models.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1992
Accession Number
ADA259039

Entities

People

  • Luis C. Cattani
  • Paul J. Eagle
  • Xin Liu
  • Zhud Lin

Organizations

  • University of Detroit Mercy

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Accuracy
  • Aircrafts
  • Algorithms
  • Angular Acceleration
  • Center Of Gravity
  • Computational Science
  • Data Science
  • Elevation
  • Equations Of State
  • Information Science
  • Kalman Filters
  • Mathematical Filters
  • Power Series
  • Stations
  • Statistical Algorithms
  • Translations
  • White Noise

Readers

  • Computational Modeling and Simulation
  • Control Systems Engineering.
  • Sensor Fusion and Tracking Systems.