Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

Several innovations are introduced for space object attitude estimation using light-curve measurements. A radiometric measurement noise model is developed to define the observation uncertainty in terms of optical, environmental, space object, and sensor parameters and is validated using experimental data. Additionally, a correlated process noise model is introduced to represent the angular acceleration dynamics. This model is used to account for the unknown inertia and body torques of agile space objects. This linear dynamics model enables the implementation of marginalized particle filters, affording computationally tractable three-degree-of-freedom Bayesian estimation. The synthesis of these novel approaches enables the estimation of attitude and angular velocity states of maneuvering space objects without a priori knowledge of initial attitude while maintaining computational tractability. Simulated results are presented for the full three-degree-of-freedom agile space object attitude estimation problem.