Photometric Attitude Estimation for Agile Space Objects with Shape Uncertainty

The problem of estimating attitude for actively maneuvering or passively rotating space objects with unknown mass properties/external torques and uncertain shape models is addressed. To account for agile space object maneuvers, angular rates are simply assumed to be random inputs (e.g., process noise), and model uncertainty is accounted for in a bias state with dynamics derived using first principles. Bayesian estimation approaches are used to estimate the resulting severely non-Gaussian and multimodal state distributions. Simulated results are given, conclusions regarding performance are made, and future work is outlined.