  This manuscript develops a simultaneous navigation and gravity estimation
strategy around a small body. The scheme combines dynamical model compensation
with a mascon gravity fit. Dynamical compensation adds the unmodeled
acceleration to the filter state. Consequently, the navigation filter is able
to generate an on-orbit position-unmodeled acceleration dataset. The available
measurements correspond to the landmarks-based navigation technique.
Accordingly, an on-board camera is able to provide landmark pixels. The
aforementioned position-unmodeled acceleration dataset serves to train a mascon
gravity model on-board while in flight. The training algorithm finds the
optimal mass values and locations using Adam gradient descent. By a careful
choice of the mascon variables and constraints projection, the masses are
ensured to be positive and within the small body shape. The numerical results
provide a comprehensive analysis on the global gravity accuracy for different
estimation scenarios.
