On the state and parameter estimation for maneuvering reentry vehicles

This paper examines several versions of the extended Kalman filter which can be used to estimate the position, velocity, and other key parameters associated with maneuvering reentry vehicles. These filters are discussed in terms of the fundamental problems of modeling accuracy, filter sophistication, and the real-time computational requirements. Techniques which adaptively increase the process noise to compensate for modeling errors during the maneuvers are examined.