Commonly applied in satellites and other complex systems, the Kalman filter (KF) is an optimal estimation strategy and many nonlinear variants have been introduced in practice. A trade-off commonly exists between optimality and robustness. In the presence of unmodeled disturbances, modeling errors, or sub-system failure, non-robust strategies can fail to correctly estimate states, resulting in failure across the system. In the context of Earth observing satellites, this can materialize as internal or environmental disturbances, operational faults, or changes to the system properties, resulting in communication or data loss with performance decline. In this paper, estimation strategies for a nonlinear satellite system are derived and evaluated. Introducing disturbances, modeling errors, and sub-system faults to the simulated dynamics, the state estimation error for each filter is calculated and compared to each other, quantifying robustness. The extended KF and extended sliding in- novation filter (ESIF) are applied, as well as two nonlinear extensions of the second-order SIF and alpha SIF, not previously applied in literature. Computational simulations are performed on an ideal satellite system undergoing an attitude regulation maneuver subjected to selected com- plications. From the results of the experiment, it was concluded that the robust strategies out-performed the conventional EKF when faults were injected, having less error between the estimated and true states.