Acceleration Characterization for Reentry Orbit Determination with Unmodeled Maneuvers

A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariance...

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Bibliographic Details
Published in:Journal of guidance, control, and dynamics control, and dynamics, 2018-07, Vol.41 (7), p.1463-1475
Main Author: Hough, Michael E
Format: Article
Language:English
Subjects:
Acceleration
Accuracy
Adaptation
Adaptive filters
Algorithms
Altitude
Bias
Computer simulation
Covariance
Dynamic pressure
Kalman filters
Model accuracy
Noise
Orbit determination
Orbital maneuvers
Reentry
Sensitivity analysis
Statistical analysis
Trajectory analysis
Velocity
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Summary:A new acceleration characterization filter is formulated for reentry orbit determination with unmodeled maneuvers. Drag and lift accelerations are treated as deterministic disturbances that can be characterized statistically and included in covariance predictions. Mean-square acceleration covariances are determined by a statistical analysis of expected maneuvers. Adaptation to the dynamic reentry environment is improved because the mean-square acceleration covariance depends on dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to offnominal trajectories.
ISSN:0731-5090
1533-3884
DOI:10.2514/1.G003359