Abstract
Nonlinear Markov acceleration models with variable process noise are implemented in an iterated recursive filter for reentry maneuver estimation. Drag and two lift acceleration components are modeled by nonlinear, first-order differential equations whose parameters are functions of position, velocity, and acceleration. Process noise amplitudes are determined with a new systematic procedure based on statistics of expected maneuvers. Adaptation to off-nominal trajectories is improved because acceleration process noise increases with dynamic pressure. Performance simulations demonstrate the accuracy and effectiveness of this adaptive acceleration filter for demanding reentry maneuvers. Monte Carlo techniques assess accuracy sensitivity to modeling assumptions and to off-nominal trajectories.
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