Application of Adaptive Filtering Methods to Maneuvering Trajectory Estimation.

摘要

The purpose of this report is to examine several modifications of extended Kalman filters which can be used to estimate the position, velocity, and other key parameters associated with maneuvering re-entry vehicles. These filters will be described and discussed in terms of the fundamental problems of modeling accuracy, filter sophistication, and the real-time computational requirements. A nine-state, extended Kalman filter based upon the maneuvering vehicle dynamics is compared with several other candidate filters. These candidate filters include a simple filter based upon polynomial dynamics decoupled with respect to the coordinates and a more complex, fully coupled, seven-state, extended Kalman filter based upon a ballistic re-entry vehicle dynamics. Techniques which adaptively increase the process noise to compensate for modeling errors during the maneuvers are examined.