Adaptive Extended Kalman Filtering Strategies for Autonomous Relative Navigation of Formation Flying Spacecraft

To enhance the capabilities of onboard autonomous guidance, navigation and control systems, this thesis presents the development of two adaptive extended Kalman filter navigation algorithms for spacecraft formation flying. The proposed adaptive filters are capable of updating the internal noise characteristics of the Kalman filter in real time, and are viable in all orbit scenarios, including highly elliptical orbits in the presence of perturbations. The first Kalman filter approach uses maximum likelihood estimation techniques to derive analytical adaptations laws for the filter, and the second approach uses an embedded fuzzy logic system based on a covariance-matching analysis of the filter residuals. Numerical simulations of three spacecraft formations are used to demonstrate that the proposed adaptive navigation algorithms are appreciably more robust to filter initialization errors, dynamics modelling deficiencies, and measurement noise than the standard extended Kalman filter.