In this work, sensor tasking refers to assigning the times and pointing directions for a sensor to collect observations of cataloged objects, in order to maintain the accuracy of the orbit estimates. Sensor tasking must consider the dynamics of the objects and uncertainty in their positions, the coordinate frame in which the sensor tasking is defined, the timing requirements for observations, the sensor capabilities, the local visibility, and constraints on the information processing and

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... cation. This research focuses on finding efficient ways to solve the sensor tasking optimization problem. First, different coordinate frames are investigated, and it is shown that the observer fixed Local Meridian Equatorial (ground-based) and Satellite Meridian Equatorial (space-based) coordinate frames provide consistent sets of pointing directions and accurate representations of orbit uncertainty for use by the optimizers in solving the sensor tasking problem. Next, two classical optimizers (greedy and Weapon-Target Assignment) which rely on convexity are compared with two Machine Learning optimizers (Ant Colony Optimization and Distributed Q-learning) which attempt to learn about the solution space in order to approximate a global optimal solution. It is shown that the learning optimizers are able to generate better solutions, while the classical optimizers are more efficient to run and require less tuning to implement. Finally, the realistic scenario where the optimization algorithm receives no feedback before it must make the next decision is introduced. The Predicted Measurement Probability (PMP) is developed, and employed in a two sensor optimization framework. The PMP is shown to provide effective feedback to the optimization algorithm regarding the observations of each sensor.