ADAPTIVE GAUSSIAN MIXTURE FILTERING FOR AUTONOMOUS CISLUNAR NAVIGATION

This thesis aims to assess the efficacy of adaptive Gaussian mixture filtering for an inertial navigation-based cislunar application. The thesis focuses on a fully autonomous system, where the navigation system is solely reliant on onboard sensors and receives no navigation information from external tracking systems. The proposed adaptive filter is tested under non-ideal conditions. Specifically, this thesis considers the challenging case where range information is unavailable, and instead, only bearings angles with respect to illuminated celestial bodies are measured. The performance of the adaptive filter is compared to the unscented Kalman filter (UKF), and the filter consistency and errors are compared. The proposed filter addresses challenges in linearization errors that accrue in the UKF measurement update equations. The adaptive filter is shown to be a consistent estimator, significantly outperforming the UKF. Considering design requirements for similar navigation missions, recommendations and practical considerations are suggested for future cislunar autonomous navigation applications