dc.title: Scalable, information-based sensor tasking for space situational awareness dc.description.abstract: Space Situational Awareness (SSA) underpins modern space operations by characterizing the space environment and objects within. An essential component of SSA is state estimation. However, even an optimal estimator will perform poorly when provided with poor measurements; sensor tasking provides a principled way to make sensing decisions. The main contribution of this thesis is presenting a scalable sensor tasking framework to maximize measurement informativeness that is suitable for the nonlinear dynamics and measurement models present in SSA. An intuitive and theoretically-grounded objective is mutual information, which quantifies the reduction in target state uncertainty by predicted measurements under a candidate tasking decision. While generally intractable, the sensor tasking problem is solved suboptimally using a submodular surrogate. Submodularity is exploited to admit a tractable, performance-bounded solution. A distributed, game-theoretic algorithm is applied to solve this problem, providing scalability and robustness. The proposed algorithm is validated using simulated data from a hypothetical cislunar sensing network.