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, 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 a predictions using Runge-Kutta integration and extrapolated regression models.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 1993
- Accession Number
- ADA270765
Entities
People
- Frank P. Kuhl
- Luis C. Cattani
- Paul J. Eagle
- Xin Liu
- Zhuo Lin
Organizations
- University of Detroit Mercy