satdatagen: a Python Library for Satellite Sensor Tasking Scheduler Support

The amount of objects in Earth's orbit is increasing rapidly, raising urgency for intensified observations of satellites and other resident space objects (RSOs) to manage space traffic and prevent collisions. Current methods for RSO detection and tracking rely on ground-based and space-based observatories with optical or radar sensors, but these telescopes require meticulous scheduling to achieve surveillance of all objects. Previous works have implemented scheduling algorithms and machine learning models that optimize the assignment of tasks to the telescopes for RSO observations. However, prior methodologies rely on different datasets, making it hard to make comparisons across methods. This thesis proposes satdatagen: a software package that generates datasets to be used as inputs to these sensor tasking schedulers. The datasets generated from the satdatagen library are intended to be used as a baseline input to satellite tasking schedulers for direct comparison of their results. They contain information about every satellite that passes in view of the sensor such as its angle of altitude and its brightness. Additional actual cloud cover data is included as well for optical telescopes that need to take visibility into account when scheduling observations. satdatagen is simple to use, and does not require excess outside knowledge from developers.